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1
Cheng, Long, Sihang Huang, Mingkun Xue, and Yangyang Bi. "A Robust Localization Algorithm Based on NLOS Identification and Classification Filtering for Wireless Sensor Network." Sensors 20, no. 22 (November 19, 2020): 6634. http://dx.doi.org/10.3390/s20226634.
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With the rapid development of information and communication technology, the wireless sensor network (WSN) has shown broad application prospects in a growing number of fields. The non-line-of-sight (NLOS) problem is the main challenge to WSN localization, which seriously reduces the positioning accuracy. In this paper, a robust localization algorithm based on NLOS identification and classification filtering for WSN is proposed to solve this problem. It is difficult to use a single filter to filter out NLOS noise in all cases since NLOS cases are extremely complicated in real scenarios. Therefore, in order to improve the robustness, we first propose a NLOS identification strategy to detect the severity of NLOS, and then NLOS situations are divided into two categories according to the severity: mild NLOS and severe NLOS. Secondly, classification filtering is performed to obtain respective position estimates. An extended Kalman filter is applied to filter line-of-sight (LOS) noise. For mild NLOS, the large outliers are clipped by the redescending score function in the robust extended Kalman filter, yielding superior performance. For severe NLOS, a severe NLOS mitigation algorithm based on LOS reconstruction is proposed, in which the average value of NLOS error is estimated and the measurements are reconstructed and corrected for subsequent positioning. Finally, an interactive multiple model algorithm is employed to obtain the final positioning result by weighting the position estimation of LOS and NLOS. Simulation and experimental results show that the proposed algorithm can effectively suppress NLOS error and obtain higher positioning accuracy when compared with existing algorithms.
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Zhang, Hao, Qing Wang, Chao Yan, Jiujing Xu, and Bo Zhang. "Research on UWB Indoor Positioning Algorithm under the Influence of Human Occlusion and Spatial NLOS." Remote Sensing 14, no. 24 (December 14, 2022): 6338. http://dx.doi.org/10.3390/rs14246338.
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Ultra-wideband (UWB) time-of-flight (TOF)-based ranging information in a non-line-of-sight (NLOS) environment can display significant forward errors, which directly affect positioning performance. NLOS has been a major factor limiting the improvement of UWB positioning accuracy and its application in complex scenarios. Therefore, in order to weaken the influence of the indoor complex environment on the NLOS environment of UWB and to further improve the performance of positioning, in this paper, we first analyze the factors and characteristics of NLOS formation in an indoor environment. The NLOS is divided into fixed NLOS influenced by spatial structure and dynamic random NLOS influenced by human occlusion. Then, the anchor LOS/NLOS information map is established by making full use of indoor spatial a priori information. On this basis, a robust adaptive extended Kalman filtering algorithm based on the anchor LOS/NLOS information map is designed, which is not only effectively able to exclude the influence of spatial NLOS, but can also optimize the random error. The proposed algorithm was validated in different experimental scenarios. The experimental results show that the positioning accuracy is better than 0.32 m in complex indoor NLOS environments.
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Liu, Ang, Shiwei Lin, Jianguo Wang, and Xiaoying Kong. "A Succinct Method for Non-Line-of-Sight Mitigation for Ultra-Wideband Indoor Positioning System." Sensors 22, no. 21 (October 27, 2022): 8247. http://dx.doi.org/10.3390/s22218247.
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Ultra-wideband (UWB) is a promising indoor position technology with centimetre-level positioning accuracy in line-of-sight (LOS) situations. However, walls and other obstacles are common in an indoor environment, which can introduce non-line-of-sight (NLOS) and deteriorate UWB positioning accuracy to the meter level. This paper proposed a succinct method to identify NLOS induced by walls and mitigate the error for improved UWB positioning with NLOS. First, NLOS is detected by a sliding window method, which can identify approximately 90% of NLOS cases in a harsh indoor environment. Then, a delay model is designed to mitigate the error of the UWB signal propagating through a wall. Finally, all the distance measurements, including LOS and NLOS, are used to calculate the mobile UWB tag position with ordinary least squares (OLS) or weighted least squares (WLS). Experiment results show that with correct NLOS indentation and delay model, the proposed method can achieve positioning accuracy in NLOS environments close to the level of LOS. Compared with OLS, WLS can further optimise the positioning results. Correct NLOS indentation, accurate delay model and proper weights in the WLS are the keys to accurate UWB positioning in NLOS environments.
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Cheng, Long, Yifan Li, Yan Wang, Yangyang Bi, Liang Feng, and Mingkun Xue. "A Triple-Filter NLOS Localization Algorithm Based on Fuzzy C-means for Wireless Sensor Networks." Sensors 19, no. 5 (March 10, 2019): 1215. http://dx.doi.org/10.3390/s19051215.
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With the rapid development of communication technology in recent years, Wireless Sensor Network (WSN) has become a promising research project. WSN is widely applied in a number of fields such as military, environmental monitoring, space exploration and so on. The non-line-of-sight (NLOS) localization is one of the most essential techniques for WSN. However, the NLOS propagation of WSN is largely influenced by many factors. Hence, a triple filters mixed Kalman Filter (KF) and Unscented Kalman Filter (UKF) voting algorithm based on Fuzzy-C-Means (FCM) and residual analysis (TF-FCM) has been proposed to cope with this problem. Firstly, an NLOS identification algorithm based on residual analysis is used to identify NLOS errors. Then, an NLOS correction algorithm based on voting and NLOS errors classification algorithm based on FCM are used to process the NLOS measurements. Hard NLOS measurements and soft NLOS measurements are classified by FCM classification. Secondly, KF and UKF are applied to filter two categories of NLOS measurements. Thirdly, maximum likelihood localization (ML) is employed to estimate the position of mobile nodes. The simulation result confirms that the accuracy and robustness of TF-FCM are better than IMM, UKF and KF. Finally, an experiment is conducted to test and verify our algorithm which obtains higher localization accuracy.
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Hao, Yukai, and Xin Qiu. "Performance Analysis of Wireless Location and Velocity Tracking of Digital Broadcast Signals Based on Extended Kalman Filter Algorithm." Complexity 2021 (February 3, 2021): 1–10. http://dx.doi.org/10.1155/2021/6655889.
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In order to improve the accuracy and reliability of wireless location in NLOS environment, a wireless location algorithm based on artificial neural network (ANN) is proposed for NLOS positioning error caused by non-line-of-sight (NLOS) propagation, such as occlusion and signal reflection. The mapping relationship between TOA and TDOA measurement data and coordinates is established. The connection weights of neural network are estimated as the state variables of nonlinear dynamic system. The multilayer perceptron network is trained by the real-time neural network training algorithm based on extended Kalman (EKF). Combined with the statistical characteristics of NLOS error, the state component NLOS bias estimation is modified to realize TDOA data reconstruction. Simulation and experimental data analysis show that the algorithm can effectively weaken the influence of NLOS error. The localization method does not depend on the specific NLOS error distribution, nor does it need LOS and NLOS recognition. It can significantly improve the mobile positioning accuracy.
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Xu, Yan Ying, Song Jian Bao, and Yu Lin Wang. "Analysis and Research of Mobile Station Location Based on NLOS Error." Applied Mechanics and Materials 713-715 (January 2015): 1460–64. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.1460.
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Existed in the work of wireless positioning error, the need to suppress NLOS (Non line of sight) transmission problem of positioning the bad influence of the NLOS system model is put forward and the novel geometric positioning model, the introduction of appropriate NLOS channels model to suppress NLOS error, and make full use of the propagation characteristics of derived meet MS (Mobile Station) coordinates equation, with two NLOS paths can only calculate the position of MS, and using only a single base Station can complete the MS positioning, overcome the base Station number too little to pinpoint the flaws of the MS. This paper also gives a method of least squares and maximum likelihood algorithm, using the NLOS paths to improve the positioning accuracy. So as to realize the movement of the MS in NLOS environment position tracking. Through the theoretical analysis and computer simulation analysis, the results show that the positioning method in NLOS environment on the effectiveness and accuracy of the MS positioning.
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Kan, Ruixiang, Mei Wang, Zou Zhou, Peng Zhang, and Hongbing Qiu. "Acoustic Signal NLOS Identification Method Based on Swarm Intelligence Optimization SVM for Indoor Acoustic Localization." Wireless Communications and Mobile Computing 2022 (May 9, 2022): 1–20. http://dx.doi.org/10.1155/2022/5210388.
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The demand for an indoor localization system is increasing, and related research is also becoming more universal. Previous works on indoor localization systems mainly focus on the acoustic signals in Line of Sight (LOS) scenario to obtain accurate localization information, but their effectiveness in Nonline of Sight (NLOS) scenario remains comparatively untouched. These works are usually less efficient as the acoustic signals often bring diffraction, refraction, scattering, energy decays, and so on in NLOS environments. So the system needs adjusting accordingly in a complex NLOS scenario based on NLOS identification results. Therefore, the identification of NLOS acoustic signal turns out to be significant in the indoor localization system. If the system only uses original support vector machine (SVM) to complete NLOS identification, the result turns out to be poor by our test. To address this challenge, we propose a novel indoor localization system, named ZKLocPro, which utilizes an advanced swarm intelligence method to optimize the traditional SVM classification model to deal with NLOS acoustic signal identification. Its results can help the system adjust the localization process if necessary in a complex NLOS scenario. Obviously, it is also significant to build our own NLOS data set, which is suitable for an indoor localization system’s situation. Specifically, four methods are added: (1) new LOS and NLOS acoustic localization signal sample production, rearrangement, and reselecting process; (2) advanced parameter optimization process; (3) elitist strategy; and (4) inertia weight nonlinear decrement. The experimental result shows that our system is efficient and performs better than state-of-the-art congeneric works even in a complex NLOS scenario.
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Yu, Xiaosheng, Peng Ji, Ying Wang, and Hao Chu. "Mean Shift-Based Mobile Localization Method in Mixed LOS/NLOS Environments for Wireless Sensor Network." Journal of Sensors 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/5325174.
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Mobile localization estimation is a significant research topic in the fields of wireless sensor network (WSN), which is of concern greatly in the past decades. Non-line-of-sight (NLOS) propagation seriously decreases the positioning accuracy if it is not considered when the mobile localization algorithm is designed. NLOS propagation has been a serious challenge. This paper presents a novel mobile localization method in order to overcome the effects of NLOS errors by utilizing the mean shift-based Kalman filter. The binary hypothesis is firstly carried out to detect the measurements which contain the NLOS errors. For NLOS propagation condition, mean shift algorithm is utilized to evaluate the means of the NLOS measurements and the data association method is proposed to mitigate the NLOS errors. Simulation results show that the proposed method can provide higher location accuracy in comparison with some traditional methods.
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Wang, Fang, Hai Tang, and Jialei Chen. "Survey on NLOS Identification and Error Mitigation for UWB Indoor Positioning." Electronics 12, no. 7 (April 2, 2023): 1678. http://dx.doi.org/10.3390/electronics12071678.
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Ultra-wideband (UWB) positioning systems often operate in a non-line-of-sight (NLOS) environment. NLOS propagation has become the main source of ultra-wideband indoor positioning errors. As such, how to identify and correct NLOS errors has become a key problem that must be solved in high-accuracy indoor positioning technology. This paper firstly describes the influence of the NLOS propagation path on localization accuracy and the generation method of ultra-wideband signals, and secondly classifies and analyzes the currently available algorithms for ultra-wideband non-line-of-sight (NLOS) identification and error suppression. For the identification of NLOS, the residual analysis judgement method, statistical feature class identification method, machine learning method and geometric feature judgement method are discussed. For the suppression of NLOS propagation errors, weighting-based methods, filtering-based methods, line-of-sight reconstruction algorithms, neural network algorithms, optimization methods with constraints, and path tracing methods are discussed. Finally, we conclude the paper and point out the problems that need to be solved in NLOS indoor positioning.
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Zhang, Hao, Qing Wang, Zehui Li, Jing Mi, and Kai Zhang. "Research on High Precision Positioning Method for Pedestrians in Indoor Complex Environments Based on UWB/IMU." Remote Sensing 15, no. 14 (July 15, 2023): 3555. http://dx.doi.org/10.3390/rs15143555.
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Location information is the core data in IoT applications, which is the essential foundation for scene interpretation and interconnection of everything, and thus high-precision positioning is becoming an immediate need. However, the non-line-of-sight (NLOS) effect of indoor complex environment on UWB signal occlusion has been a major factor limiting the improvement in ultra-wideband (UWB) positioning accuracy, and the optimization of NLOS error has not yet been studied in a targeted manner. To this end, this paper deeply analyzes indoor scenes, divides NLOS into two forms of spatial occlusion and human occlusion, and proposes a particle filtering algorithm based on LOS/NLOS mapping and NLOS error optimization. This algorithm is targeted to optimize the influence of two different forms of NLOS, using spatial a priori information to accurately judge the LOS/NLOS situation of the anchor, optimizing the NLOS anchor ranging using IMU to project the virtual position, judging whether the LOS anchor is affected by human occlusion, and correcting the affected LOS anchor using the established human occlusion error model. Through experimental verification, the algorithm can effectively suppress two different NLOS errors of spatial structure and human occlusion and can achieve continuous and reliable high-precision positioning and tracking in complex indoor environments.
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Xu, Wenjie, Zhenkai Zhang, and Boon-Chong Seet. "Measurement error compensation method for TDOA-based localization under non-line-of-sight conditions." Measurement Science and Technology 36, no. 4 (April 10, 2025): 045116. https://doi.org/10.1088/1361-6501/adc760.
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Abstract When signals propagate along non-line-of-sight (NLOS) paths, measurement results between sensor network nodes will experience significant errors. Most of the existing time-difference-of-arrival (TDOA) localization methods with higher accuracy require prior knowledge of the NLOS environment or use complex calculation methods such as convex optimization. A prior-knowledge-free method with high efficiency is proposed. Considering that the influence of NLOS environment on TDOA localization is determined by the differences of NLOS errors between each two nodes, a balance parameter will be introduced into the measurement information of the node with the most dispersed NLOS error. This parameter is designed to minimize the variance of the array of NLOS errors, in order to achieve a compensatory effect. The node with the most dispersed NLOS error is selected by using the relationship between the cost function of approximate maximum likelihood estimation and the variance of NLOS errors. A two-step weighting method is proposed to obtain an initial estimate of the target’s coordinates for calculating the value of the balance parameter. Finally, the balance parameter is added to the TDOA measurement information to estimate the target position. Through simulations and experiments, it is proved that the proposed method has high positioning accuracy and timeliness under different NLOS conditions.
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Hu, Shuo, Lixin Guo, Zhongyu Liu, and Shuaishuai Gao. "Multipath-Assisted Ultra-Wideband Vehicle Localization in Underground Parking Environment Using Ray-Tracing." Sensors 25, no. 7 (March 26, 2025): 2082. https://doi.org/10.3390/s25072082.
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In complex underground parking scenarios, non-line-of-sight (NLOS) obstructions significantly impede positioning signals, presenting substantial challenges for accurate vehicle localization. While traditional positioning approaches primarily focus on mitigating NLOS effects to enhance accuracy, this research adopts an alternative perspective by leveraging NLOS propagation as valuable information, enabling precise positioning in NLOS-dominated environments. We introduce an innovative NLOS positioning framework based on the generalized source (GS) technique, which employs ray-tracing (RT) to transform NLOS paths into equivalent line-of-sight (LOS) paths. A novel GS filtering and weighting strategy to establish initial weights for the nonlinear equation system. To combat significant NLOS noise interference, a robust iterative reweighted least squares (W-IRLS) method synergizes initial weights with optimal position estimation. Integrating ultra-wideband (UWB) delay and angular measurements, four distinct localization modes based on W-IRLS are developed: angle-of-arrival (AOA), time-of-arrival (TOA), AOA/TOA hybrid, and AOA/time-difference-of-arrival (TDOA) hybrid. The comprehensive experimental and simulation results validate the exceptional effectiveness and robustness of the proposed NLOS positioning framework, demonstrating positioning accuracy up to 0.14 m in specific scenarios. This research not only advances the state of the art in NLOS positioning but also establishes a robust foundation for high-precision localization in challenging environments.
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Long, Shan, Zhe Cui, and Fei Song. "A Two-Step Optimizing Algorithm for TOA Real-Time Dynamic Localization in NLOS Environment." Applied Mechanics and Materials 347-350 (August 2013): 3604–8. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3604.
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Non-line-of-sight (NLOS) is one of the main factors that affect the ranging accuracy in wireless localization. This paper proposes a two-step optimizing algorithm for TOA real-time tracking in NLOS environment. Step one, use weighted least-squares (WLS) algorithm, combined with the NLOS identification informations, to mitigate NLOS bias. Step two, utilize Kalman filtering to optimize the localization results. Simulation results show that the proposed two-step algorithm can obtain better localization accuracy, especially when there are serious NLOS obstructions.
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Song, Bo, Sheng-Lin Li, Mian Tan, and Qing-Hui Ren. "A Fast Imbalanced Binary Classification Approach to NLOS Identification in UWB Positioning." Mathematical Problems in Engineering 2018 (December 2, 2018): 1–8. http://dx.doi.org/10.1155/2018/1580147.
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Non-line-of-sight (NLOS) propagation is an important factor affecting the positioning accuracy of ultra-wide band (UWB). In order to mitigate the NLOS ranging error caused by various obstacles in UWB ranging process, some scholars have applied machine learning methods such as support vector machine and support vector data description to the identification NLOS signals for mitigation NLOS error in recent years. Therefore, the identification of NLOS signals is of great significance in UWB positioning. The traditional machine learning method is based on the assumption that the number of samples of the line-of-sight (LOS) and NLOS signals are balanced. However, in reality, the number of LOS signals in UWB positioning is much larger than the NLOS signals. So the samples are characterized by class-imbalance. In response to this fact, we applied a fast imbalanced binary classification method based on moments (MIBC) to identify NLOS signals. The method uses the mean and covariance of the two first moments of the LOS signal samples to represent its probability distribution and then uses the probability distribution and all a small amount of NLOS signal samples to establish a model. This method does not depend on the number of LOS signals and is suitable for dealing with the problem of classification of the imbalance between the number of LOS and NLOS signals. Numerical simulations also verify that the method has better performance than LS-SVM and SVDD.
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Suzuki, Taro, and Yoshiharu Amano. "NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning." Sensors 21, no. 7 (April 3, 2021): 2503. http://dx.doi.org/10.3390/s21072503.
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This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.
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Wang, Yan, Xuehan Wu, and Long Cheng. "A Novel Non-Line-of-Sight Indoor Localization Method for Wireless Sensor Networks." Journal of Sensors 2018 (September 27, 2018): 1–10. http://dx.doi.org/10.1155/2018/3715372.
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The localization technology is the essential requirement of constructing a smart building and smart city. It is one of the most important technologies for wireless sensor networks (WSNs). However, when WSNs are deployed in harsh indoor environments, obstacles can result in non-line-of-sight (NLOS) propagation. In addition, NLOS propagation can seriously reduce localization accuracy. In this paper, we propose a NLOS localization method based on residual analysis to reduce the influence of NLOS error. The time of arrival (TOA) measurement model is used to estimate the distance. Then, the NLOS measurement is identified through the residual analysis method. Finally, this paper uses the LOS measurements to establish the localization objective function and proposes the particle swarm optimization with a constriction factor (PSO-C) method to compute the position of an unknown node. Simulation results show that the proposed method not only effectively identifies the LOS/NLOS propagation condition but also reduces the influence of NLOS error.
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Liu, Jingrong, Zhongliang Deng, and Enwen Hu. "An NLOS Ranging Error Mitigation Method for 5G Positioning in Indoor Environments." Applied Sciences 14, no. 9 (April 30, 2024): 3830. http://dx.doi.org/10.3390/app14093830.
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Positioning based on wireless signals such as mobile communication networks has become an important means to provide high-precision location services in environments where satellite signals are blocked. In complex environments such as indoors and underground, wireless signal propagation is obstructed and non-line-of-sight (NLOS) phenomena appear due to serious occlusion and reflection. The time delay caused by NLOS effects has little impact on communication system but can significantly increase positioning errors in positioning systems. Therefore, the effective suppression of NLOS errors is crucial to improving 5G positioning accuracy. To address the insufficient feature extraction of existing NLOS error suppression methods, the neglect of residual NLOS measurement errors, and poor stability of position estimation results, this paper innovatively proposes an NLOS mitigation and location estimation method for 5G positioning terminals. Simulation and experimental test results demonstrate that the proposed method outperforms the comparative methods both theoretically and practically, achieving an average positioning accuracy of 1.85 m in complex indoor NLOS environments. The method proposed in this paper provides a new strategy for NLOS error suppression in indoor 5G positioning, which can significantly contribute to high-precision location services based on commercial 5G networks.
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Wang, Lei, Ruizhi Chen, Lili Shen, Haiyang Qiu, Ming Li, Peng Zhang, and Yuanjin Pan. "NLOS Mitigation in Sparse Anchor Environments with the Misclosure Check Algorithm." Remote Sensing 11, no. 7 (March 31, 2019): 773. http://dx.doi.org/10.3390/rs11070773.
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The presence of None-line-of-sight (NLOS) is one of the major challenging issues in time of arrival (TOA) based source localization, especially for the sparse anchor scenarios. Sparse anchors can reduce the system deployment cost, so this has become increasingly popular in the source location. However, fewer anchors bring new challenges to ensure localization precision and reliability, especially in NLOS environments. The maximum likelihood (ML) estimation is the most popular location estimator for its simplicity and efficiency, while it becomes extremely difficult to reliably identify the NLOS measurements when the redundant observations are not enough. In this study, we proposed an NLOS detection algorithm called misclosure check (MC) to overcome this issue, which intends to provide a more reliable location in the sparse anchor environment. The MC algorithm checks the misclosure of different triangles and then obtains the possible NLOS from these misclosures. By forming multiple misclosure conditions, the MC algorithm can identify NLOS measurements reliably, even in a sparse anchor environment. The performance of the MC algorithm is evaluated in a typical sparse anchor environment and the results indicate that the MC algorithm achieves promising NLOS identification capacity without abundant redundant measurements. The real data test also confirmed that the MC algorithm achieves better position precision than other three robust location estimators in an NLOS environment since it can correctly identify more NLOS measurements.
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Guo, Yihan, Simone Zocca, Paolo Dabove, and Fabio Dovis. "A Post-Processing Multipath/NLoS Bias Estimation Method Based on DBSCAN." Sensors 24, no. 8 (April 19, 2024): 2611. http://dx.doi.org/10.3390/s24082611.
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Positioning based on Global Navigation Satellite Systems (GNSSs) in urban environments always suffers from multipath and Non-Line-of-Sight (NLoS) effects. In such conditions, the GNSS pseudorange measurements can be affected by biases disrupting the GNSS-based applications. Many efforts have been devoted to detecting and mitigating the effects of multipath/NLoS, but the identification and classification of such events are still challenging. This research proposes a method for the post-processing estimation of pseudorange biases resulting from multipath/NLoS effects. Providing estimated pseudorange biases due to multipath/NLoS effects serves two main purposes. Firstly, machine learning-based techniques can leverage accurately estimated pseudorange biases as training data to detect and mitigate multipath/NLoS effects. Secondly, these accurately estimated pseudorange biases can serve as a benchmark for evaluating the effectiveness of the methods proposed to detect multipath/NLoS effects. The estimation is achieved by extracting the multipath/NLoS biases from pseudoranges using a clustering algorithm named Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The performance is demonstrated using two real-world data collections in multipath/NLoS scenarios for both static and dynamic conditions. Since there is no ground truth for the pseudorange biases due to the multipath/NLoS scenarios, the proposed method is validated based on the positioning performance. Positioning solutions are computed by subtracting the estimated biases from the raw pseudoranges and comparing them to the ground truth.
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Zheng, Xingyu, Ruizhi Chen, Liang Chen, Lei Wang, Yue Yu, Zhenbing Zhang, Wei Li, Yu Pei, Dewen Wu, and Yanlin Ruan. "A Novel Device-Free Positioning Method Based on Wi-Fi CSI with NLOS Detection and Bayes Classification." Remote Sensing 15, no. 10 (May 21, 2023): 2676. http://dx.doi.org/10.3390/rs15102676.
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Device-free wireless localization based on Wi-Fi channel state information (CSI) is an emerging technique that could estimate users’ indoor locations without invading their privacy or requiring special equipment. It deduces the position of a person by analyzing the influence on the CSI of Wi-Fi signals. When pedestrians block the signals between the transceivers, the non-line-of-sight (NLOS) transmission occurs. It should be noted that NLOS has been a significant factor restricting the device-free positioning accuracy due to signal reduction and abnormalities during multipath propagation. For this problem, we analyzed the NLOS effect in an indoor environment and found that the position error in the LOS condition is different from the NLOS condition. Then, two empirical models, namely, a CSI passive positioning model and a CSI NLOS/LOS detection model, have been derived empirically with extensive study, which can obtain better robustness identified results in the case of NLOS and LOS conditions. An algorithm called SVM-NB (Support Vector Machine-Naive Bayes) is proposed to integrate the SVM NLOS detection model with the Naive Bayes fingerprint method to narrow the matching area and improve position accuracy. The NLOS identification precision is better than 97%. The proposed method achieves localization accuracy of 0.82 and 0.73 m in laboratory and corridor scenes, respectively. Compared to the Bayes method, our tests showed that the positioning accuracy of the NLOS condition is improved by 28.7% and that of the LOS condition by 26.2%.
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Tian, Shiwei, Luwen Zhao, and Guangxia Li. "A Support Vector Data Description Approach to NLOS Identification in UWB Positioning." Mathematical Problems in Engineering 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/963418.
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Non-line-of-sight (NLOS) propagation is one of the most important challenges in radio positioning, and, in recent years, significant attention has been drawn to the identification and mitigation of NLOS signals. This paper focuses on the identification of NLOS signals. The authors consider the NLOS identification problem as a one-class classification problem and apply the support vector data description (SVDD), providing accurate data descriptions utilizing kernel techniques, to perform NLOS identification in ultrawide bandwidth (UWB) positioning. Our work is based on the fact that some features extracted from the received signal waveforms, such as the kurtosis, the mean excess delay spread, and the root mean square delay spread, are different between line-of-sight (LOS) and NLOS signals. Numerical simulations are performed to demonstrate the performance, using a dataset derived from a measurement campaign.
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Janakiraman, Sengathir. "An improved rank criterion-based NLOS node detection mechanism in VANETs." International Journal of Intelligent Unmanned Systems 9, no. 1 (July 16, 2020): 1–15. http://dx.doi.org/10.1108/ijius-12-2019-0072.
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PurposeAn Improved Rank Criterion-based NLOS node Detection Mechanism (IRC-NLOS-DM) is proposed based on the benefits of a reputation model for effective localization of NLOS nodes during the dynamic exchange of emergency messages in critical situations.Design/methodology/approachThis proposed IRC-NLOS-DM scheme derives the benefits of a reputation model that influentially localizes the NLOS nodes under dynamic exchange of emergency messages. This proposed IRC-NLOS-DM scheme is an attempt to resolve the issues with the routing protocols that aids in warning message delivery of vehicles that are facing NLOS situations with the influence of channel contention and broadcast storm. It is developed for increasing the warning packet delivery rate with minimized overhead, delay and channel utilization.FindingsThe simulation results of the proposed IRC-NLOS-DM scheme confirmed the excellence of the proposed IRC-NLOS-DM over the existing works investigated based on the channel utilization rate, neighborhood prediction rate and emergency message forwarding rate.Practical implicationsIt is proposed for reliable warning message delivery in Vehicular Ad hoc Networks (VANETs) which is referred as the specialized category of mobile ad hoc network application that influences Intelligent Transportation Systems (ITS) and wireless communications. It is proposed for implementing vehicle safety applications for constructing a least cluttered and a secure environment on the road.Originality/valueIt is contributed as a significant mechanism for facilitating reliable dissemination of emergency messages between the vehicular nodes, which is essential in the critical environment to facilitate a risk-free environment. It also aids in creating a reliable environment for accurate localization of Non-Line of Sight (NLOS) nodes that intentionally introduces the issues of broadcasting storm and channel congestion during the process of emergency message exchanges.
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He, Chengwen, Yunbin Yuan, and Bingfeng Tan. "Constrained L1-Norm Minimization Method for Range-Based Source Localization under Mixed Sparse LOS/NLOS Environments." Sensors 21, no. 4 (February 13, 2021): 1321. http://dx.doi.org/10.3390/s21041321.
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Under mixed sparse line-of-sight/non-line-of-sight (LOS/NLOS) conditions, how to quickly achieve high positioning accuracy is still a challenging task and a critical problem in the last dozen years. To settle this problem, we propose a constrained L1 norm minimization method which can reduce the effects of NLOS bias for improve positioning accuracy and speed up calculation via an iterative method. We can transform the TOA-based positioning problem into a sparse optimization one under mixed sparse LOS/NLOS conditions if we consider NLOS bias as outliers. Thus, a relatively good method to deal with sparse localization problem is L1 norm. Compared with some existing methods, the proposed method not only has the advantages of simple and intuitive principle, but also can neglect NLOS status and corresponding NLOS errors. Experimental results show that our algorithm performs well in terms of computational time and positioning accuracy.
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Li, Jinkun, Chundi Xiu, Feng Wang, Maria S. Selezneva, and Dongkai Yang. "Fuzzy Comprehensive Evaluation based NLOS Identification for UWB Indoor Positioning." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4-2024 (October 18, 2024): 485–91. http://dx.doi.org/10.5194/isprs-annals-x-4-2024-485-2024.
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Abstract. Ultra-wideband (UWB) positioning technology stands out from many indoor positioning technologies with its advantages of high precision. However, non-line-of-sight (NLOS) propagate leads to heavy range error and reduces position accuracy, this paper proposes a NLOS identification method based on channel impulse response (CIR), which includes three stages. Firstly, CIR based feature selection is carried out, which includes correlation analysis of calculated features. Secondly, fuzzy comprehensive evaluation model is introduced to NLOS identification. Finally, time of arrival (TOA) based location estimation is realized after NLOS ranging error mitigation. Simulation results show that the average identification rate of NLOS can exceed 86%, and the average positioning error can be reduced by about 0.5m.
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Cheng, Long, Mingkun Xue, Ze Liu, and Yong Wang. "A Robust Tracking Algorithm Based on a Probability Data Association for a Wireless Sensor Network." Applied Sciences 10, no. 1 (December 18, 2019): 6. http://dx.doi.org/10.3390/app10010006.
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As one of the core technologies of the Internet of Things, wireless sensor network technology is widely used in indoor localization systems. Considering that sensors can be deployed to non-line-of-sight (NLOS) environments to collect information, wireless sensor network technology is used to locate positions in complex NLOS environments to meet the growing positioning needs of people. In this paper, we propose a novel time of arrival (TOA)-based localization scheme. We regard the line-of-sight (LOS) environment and non-line-of-sight environment in wireless positioning as a Markov process with two interactive models. In the NLOS model, we propose a modified probabilistic data association (MPDA) algorithm to reduce the NLOS errors in position estimation. After the NLOS recognition, if the number of correct positions is zero continuously, it will lead to inaccurate localization. In this paper, the NLOS tracer method is proposed to solve this problem to improve the robustness of the probabilistic data association algorithm. The simulation and experimental results show that the proposed algorithm can mitigate the influence of NLOS errors and achieve a higher localization accuracy when compared with the existing methods.
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Wang, Yan, Yang Yan, Zhengjian Li, and Long Cheng. "A Mobile Localization Method in Smart Indoor Environment Using Polynomial Fitting for Wireless Sensor Network." Journal of Sensors 2020 (January 7, 2020): 1–17. http://dx.doi.org/10.1155/2020/6787252.
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The main factor affecting the localization accuracy is nonline of sight (NLOS) error which is caused by the complicated indoor environment such as obstacles and walls. To obviously alleviate NLOS effects, a polynomial fitting-based adjusted Kalman filter (PF-AKF) method in a wireless sensor network (WSN) framework is proposed in this paper. The method employs polynomial fitting to accomplish both NLOS identification and distance prediction. Rather than employing standard deviation of all historical data as NLOS detection threshold, the proposed method identifies NLOS via deviation between fitted curve and measurements. Then, it processes the measurements with adjusted Kalman filter (AKF), conducting weighting filter in the case of NLOS condition. Simulations compare the proposed method with Kalman filter (KF), adjusted Kalman filter (AKF), and Kalman-based interacting multiple model (K-IMM) algorithms, and the results demonstrate the superior performance of the proposed method. Moreover, experimental results obtained from a real indoor environment validate the simulation results.
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Wang, Kai, and Cheng Yang. "Analysis of Machine Learning-Based NLOS Signal Identification Algorithm for UWB Indoor Localization Using CIR Waveform Features." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4-2024 (October 21, 2024): 705–10. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-2024-705-2024.
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Abstract. Ultra-wideband (UWB) technology stands out among numerous indoor positioning techniques due to its high operating frequency, low interception capability, resistance to multipath effects, and strong penetration. The UWB uses the time-of-arrival (TOA) to estimate the distance between the transmitter and receiver anchors in centimeter accuracy. However, in complex indoor positioning environments, obstacles such as walls, glass windows, metal plates, and wooden doors may block and reflect signals, inevitably causing non-line-of-sight (NLOS) errors that significantly affect positioning accuracy. The NLOS signal has lower signal energy due to the reflections. Thus, the channel impulse responses (CIR) from NLOS and LOS are different. To address the NLOS signal identification issue in UWB positioning, we utilize UWB CIR data collected from various positioning scenarios as the data source. CIR waveform input features are provided for the NLOS signal recognition model, and four machine learning models—Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), K-Nearest Neighbors (KNN), and XGBoost—are trained and optimized for NLOS signal recognition. The aim of the study is to analyze the performance of different machine learning algorithms for NLOS signal recognition in UWB indoor localization using these features. Experimental results indicate that machine learning-based NLOS signal recognition algorithms can achieve an accuracy of approximately 77.46%, precision of 80.46%, and an F1 score of 0.81. Among the four models, the XGBoost model demonstrates generally better recognition performance.
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Wan, Pengwu, Jian Wei, Jin Wang, and Qiongdan Huang. "Wireless Sensor Network-Based Rigid Body Localization for NLOS Parameter Estimation." Sensors 22, no. 18 (September 8, 2022): 6810. http://dx.doi.org/10.3390/s22186810.
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In wireless sensor network (WSN)-based rigid body localization (RBL) systems, the non-line-of-sight (NLOS) propagation of the wireless signals leads to severe performance deterioration. This paper focuses on the RBL problem under the NLOS environment based on the time of arrival (TOA) measurement between the sensors fixed on the rigid body and the anchors, where the NLOS parameters are estimated to improve the RBL performance. Without any prior information about the NLOS environment, the highly non-linear and non-convex RBL problem is transformed into a difference of convex (DC) programming, which can be solved by using the concave–convex procedure (CCCP) to determine the position of the rigid body sensors and the NLOS parameters. To avoid error accumulation, the obtained NLOS parameters are utilized to refine the localization performance of the rigid body sensors. Then, the accurate position and the orientation of the rigid body in two-Dimensional space are obtained according to the relative deflection angle method. To reduce the computational complexity, the singular value decomposition (SVD) method is employed to solve the problem in three-Dimensional space. Simulation results show that the proposed method can effectively improve the performance of the rigid body localization based on the wireless sensor network in NLOS environment.
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Wang, Yan, Yang Cheng, and Long Cheng. "Fusion Localization Algorithm Based on Robust IMM Model Combined with Semi-Definite Programming." Actuators 11, no. 6 (May 29, 2022): 146. http://dx.doi.org/10.3390/act11060146.
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With the continuous development of wireless sensor network (WSN) technology, WSN has gradually become one of the key technologies of the Internet, and is widely used in indoor target location technology. However, the obstacles will have a great influence on the distance measurement, and it will result in a large positioning error. Therefore, how to deal with the non-line-of-sight (NLOS) error becomes an important problem. In this paper, Interacting Multiple Model (IMM) was used to identify NOLS/LOS. The NLOS probability was calculated by Markov transform probability, and the likelihood function was calculated by extended Kalman filter (EKF). The NLOS probability was compared with the LOS probability to judge whether the measurement contained the NLOS error. A robust algorithm combining IMM model with semidefinite programming (IMM-SDP) was proposed. The improved convex programming algorithm was proposed to reduce the NLOS error. Simulation and experimental results showed that the proposed algorithm can effectively reduce the influence of NLOS error and achieve higher positioning accuracy compared with the existing positioning methods.
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Tiwari, Smita, Donglin Wang, Michel Fattouche, and Fadhel Ghannouchi. "A Hybrid RSS/TOA Method for 3D Positioning in an Indoor Environment." ISRN Signal Processing 2012 (March 1, 2012): 1–9. http://dx.doi.org/10.5402/2012/503707.
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This paper investigates 3D positioning in an indoor line of sight (LOS) and nonline of sight (NLOS) combined environment. It is a known fact that time-of-arrival-(TOA-) based positioning outperforms other techniques in LOS environments; however, multipath in an indoor environment, especially NLOS multipath, significantly decreases the accuracy of TOA positioning. On the other hand, received-signal-strength-(RSS-) based positioning is not affected so much by NLOS multipath as long as the propagation attenuation can be correctly estimated and the multipath effects have been compensated for. Based on this fact, a hybrid weighted least square (HWLS) RSS/TOA method is proposed for target positioning in an indoor LOS/NLOS environment. The identification of LOS/NLOS path is implemented by using Nakagami distribution. An experiment is conducted in the iRadio lab, in the ICT building at the University of Calgary, in order to (i) demonstrate the availability of Nakagami distribution for the identification of LOS and NLOS path, (ii) estimate the pass loss exponent for RSS technique, and (iii) verify our proposed scheme.
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Chen, Jiajing, Xuefeng Yin, Li Tian, Nan Zhang, Yongyu He, Xiang Cheng, Weiming Duan, and Silvia Ruiz Boqué. "Measurement-Based LoS/NLoS Channel Modeling for Hot-Spot Urban Scenarios in UMTS Networks." International Journal of Antennas and Propagation 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/454976.
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A measurement campaign is introduced for modeling radio channels with either line-of-sight (LoS) or non-line-of-sight (NLoS) connection between user equipment (UE) and NodeB (NB) in an operating universal mobile telecommunications system. A space-alternating generalized expectation-maximization (SAGE) algorithm is applied to estimate the delays and the complex attenuations of multipath components from the obtained channel impulse responses. Based on a novel LoS detection method of multipath parameter estimates, channels are classified into LoS and NLoS categories. Deterministic models which are named “channel maps” and fading statistical models have been constructed for LoS and NLoS, respectively. In addition, statistics of new parameters, such as the distance between the NB and the UE in LoS/NLoS scenarios, the life-distance of LoS channel, the LoS existence probability per location and per NB, the power variation at LoS to NLoS transition and vice versa, and the transition duration, are extracted. These models are applicable for designing and performance evaluation of transmission techniques or systems used by distinguishing the LoS and NLoS channels.
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Zhang, Ke, Baiyu Li, Xiangwei Zhu, Huaming Chen, and Guangfu Sun. "NLOS Signal Detection Based on Single Orthogonal Dual-Polarized GNSS Antenna." International Journal of Antennas and Propagation 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/8548427.
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Nowadays users have a high demand for the accuracy of position and velocity, but errors caused by non-line-of-sight (NLOS) signals cannot be removed effectively. Since the GNSS signal is right-hand circular polarized (RHCP), the axial ratio of the strong NLOS signal is larger than that of the Line-of-Sight (LOS) signal. Based on the difference of the axial ratio, a method for NLOS signal detection using single orthogonal dual-polarized antenna is proposed. The antenna has two channels to receive two orthogonal linear polarized components of the incoming signals. Parallel cross-cancellation is used to remove the LOS signal while maintaining most of the NLOS signals from the receiving signals. The residual NLOS signals are then detected by conventional GNSS digital processor in real time without any prior knowledge of their characteristics. The proposed method makes use of the polarization and spatial information and can detect long delay NLOS signal by miniature and inexpensive receiver GNSS. The effectiveness of the proposed method is confirmed by simulation data.
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Hu, Shuo, Lixin Guo, and Zhongyu Liu. "A Ray-Tracing-Based Single-Site Localization Method for Non-Line-of-Sight Environments." Sensors 24, no. 24 (December 11, 2024): 7925. https://doi.org/10.3390/s24247925.
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Localization accuracy in non-line-of-sight (NLOS) scenarios is often hindered by the complex nature of multipath propagation. Traditional approaches typically focus on NLOS node identification and error mitigation techniques. However, the intricacies of NLOS localization are intrinsically tied to propagation challenges. In this paper, we propose a novel single-site localization method tailored for complex multipath NLOS environments, leveraging only angle-of-arrival (AOA) estimates in conjunction with a ray-tracing (RT) algorithm. The method transforms NLOS paths into equivalent line-of-sight (LOS) paths through the generation of generalized sources (GSs) via ray tracing. A novel weighting mechanism for GSs is introduced, which, when combined with an iteratively reweighted least squares (IRLS) estimator, significantly improves the localization accuracy of non-cooperative target sources. Furthermore, a multipath similarity displacement matrix (MSDM) is incorporated to enhance accuracy in regions with pronounced multipath fluctuations. Simulation results validate the efficacy of the proposed algorithm, achieving localization performance that approaches the Cramér–Rao lower bound (CRLB), even in challenging NLOS scenarios.
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Wang, Peng, Xin Xiang, Rui Wang, Pengyu Dong, and Qiao Li. "A Design of NLOS Communication Scheme Based on SC-FDE with Cyclic Suffix for UAV Payload Communication." Drones 8, no. 11 (November 6, 2024): 648. http://dx.doi.org/10.3390/drones8110648.
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Non-line-of-sight (NLOS) communication with severe loss always leads to performance degradation in unmanned aerial vehicle (UAV) payload communication. In this paper, a UAV NLOS communication scheme based on single-carrier frequency domain equalization with cyclic prefix and cyclic suffix (CP/CS-SC-FDE) is designed. First, the reasons behind the generation of later intersymbol interference (LISI) in UAV NLOS communication are investigated. Then, the frame structure of conventional single-carrier frequency domain equalization with cyclic prefix (CP-SC-FDE) is improved, and the UAV NLOS communication frame structure based on cyclic prefix (CP) and cyclic suffix (CS) is designed. Furthermore, a channel estimation algorithm applicable to this scheme is proposed. The numerical results show that this UAV communication scheme can eliminate intersymbol interference (ISI) in NLOS communication. Compared with the conventional CP-SC-FDE system, this scheme can achieve excellent performance in the Rayleigh channel and other standard NLOS channels. In the CP/CS-SC-FDE system, the BER result is similar to that under ideal synchronization.
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Li, Jinwang, Tongyue Gao, Xiaobing Wang, Weiping Guo, and Daizhuang Bai. "Study on the UWB location algorithm in the NLOS environment." Journal of Physics: Conference Series 2400, no. 1 (December 1, 2022): 012043. http://dx.doi.org/10.1088/1742-6596/2400/1/012043.
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Abstract At present, people spend most of their time indoors, so it is necessary to study high-precision positioning. Ultra wide band (UWB) can obtain the ranging accuracy with centimeter-level error. However, since the indoor environment is more complex than the outdoor environment, positioning errors tend to be generated during the UWB positioning due to the influence of non-line of sight (NLOS). Therefore, this paper investigates how to identify the NLOS environment and reduce the NLOS error. This paper proposes a method to determine the line of sight (LOS) environment credibility and uses it to determine the NLOS environment. The data with NLOS error is determined according to the standard deviation of the residual between the square of the predicted ranging value and the square of the measured ranging value. Then the data with NLOS error is compensated by complementary filtering. Finally, the compensated measurement data and the optimal estimation of the state at the previous moment are imported into the Kalman filter (KF) to determine the optimal estimation at the current moment.
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Dahiru Buhari, Mohammed, Tri Bagus Susilo, Irfan Khan, and Bashir Olaniyi Sadiq. "Statistical LOS/NLOS Classification for UWB Channels." KIU Journal of Science, Engineering and Technology 2, no. 1 (April 4, 2023): 32–38. http://dx.doi.org/10.59568/kjset-2023-2-1-05.
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Ultrawideband (UWB) technology has attracted a lot of attention for indoor and outdoor positioning systems due to its high accuracy and robustness in non-line-of-sight (NLOS) environments. However, UWB signals are affected by multipath propagation which causes errors in localization. To overcome this problem, researchers have proposed various techniques for NLOS identification and mitigation. One of the approaches is statistical LOS/NLOS classification, which uses statistical parameters of the received signal to distinguish between LOS and NLOS channels. In this paper, we formulated several techniques which can be used for effectively classifying Line of Sight (LOS) channel from a Non-Line of Sight (NLOS) channel. Various parameters obtained from Channel Impulse Response (CIR) like Skewness, Kurtosis, Root Mean Squared Delay Spread (RDS), Mean Excess Delay (MED), Energy, Energy Ratio and Mean of Covariance Matrix are used for channel classification. In addition to this, the Joint Probability Density Functions (PDFs) of various parameters are used to improve the accuracy of UWB LOS/NLOS channel classification. Two different criteria-Likelihood Ratio and Hypothesis Test are used for the identification of channel.
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Guo, Junqi, and Yang Wang. "Efficient AOA Estimation and NLOS Signal Utilization for LEO Constellation-Based Positioning Using Satellite Ephemeris Information." Applied Sciences 15, no. 3 (January 22, 2025): 1080. https://doi.org/10.3390/app15031080.
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As large-scale low Earth orbit (LEO) constellations continue to expand, the potential of their signal strength for positioning applications should be fully leveraged. For high-precision angle of arrival (AOA) estimation, current spectrum search algorithms are computationally expensive. To address this, we propose a method that downscales the 2D joint spectrum search algorithm by incorporating satellite ephemeris a priori information. The proposed algorithm efficiently and accurately determines the azimuth and elevation angles of NLOS (non-line-of-sight) signals. Furthermore, an NLOS virtual satellite construction method is introduced for integrating NLOS satellite data into the positioning system using previously estimated azimuth and elevation angles. Simulation experiments, conducted with a uniform planar array antenna in environments containing both LOS (line-of-sight) and NLOS signals, demonstrate the effectiveness of the proposed solution. The results show that the azimuth determination algorithm reduces computational complexity without sacrificing accuracy, while the NLOS virtual satellite construction method significantly enhances positioning accuracy in NLOS environments. The geometric dilution of precision (GDOP) improved significantly, decreasing from values exceeding 10 to an average of less than 1.42.
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Cheng, Long, Yong Wang, Mingkun Xue, and Yangyang Bi. "An Indoor Robust Localization Algorithm Based on Data Association Technique." Sensors 20, no. 22 (November 18, 2020): 6598. http://dx.doi.org/10.3390/s20226598.
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As a key technology of the Internet of Things, wireless sensor network (WSN) has been used widely in indoor localization systems. However, when the sensor is transmitting signals, it is affected by the non-line-of-sight (NLOS) transmission, and the accuracy of the positioning result is decreased. Therefore, solving the problem of NLOS positioning has become a major focus for indoor positioning. This paper focuses on solving the problem of NLOS transmission that reduces positioning accuracy in indoor positioning. We divided the anchor nodes into several groups and obtained the position information of the target node for each group through the maximum likelihood estimation (MLE). By identifying the NLOS method, a part of the position estimates polluted by NLOS transmission was discarded. For the position estimates that passed the hypothesis testing, a corresponding poly-probability matrix was established, and the probability of each position estimate from line-of-sight (LOS) and NLOS was calculated. The position of the target was obtained by combining the probability with the position estimate. In addition, we also considered the case where there was no continuous position estimation through hypothesis testing and through the NLOS tracking method to avoid positioning errors. Simulation and experimental results show that the algorithm proposed has higher positioning accuracy and higher robustness than other algorithms.
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Yang, Hongchao, Yunjia Wang, Shenglei Xu, Jingxue Bi, Haonan Jia, and Cheekiat Seow. "Ultra-Wideband Ranging Error Mitigation with Novel Channel Impulse Response Feature Parameters and Two-Step Non-Line-of-Sight Identification." Sensors 24, no. 5 (March 6, 2024): 1703. http://dx.doi.org/10.3390/s24051703.
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The effective identification and mitigation of non-line-of-sight (NLOS) ranging errors are essential for achieving high-precision positioning and navigation with ultra-wideband (UWB) technology in harsh indoor environments. In this paper, an efficient UWB ranging-error mitigation strategy that uses novel channel impulse response parameters based on the results of a two-step NLOS identification, composed of a decision tree and feedforward neural network, is proposed to realize indoor locations. NLOS ranging errors are classified into three types, and corresponding mitigation strategies and recall mechanisms are developed, which are also extended to partial line-of-sight (LOS) errors. Extensive experiments involving three obstacles (humans, walls, and glass) and two sites show an average NLOS identification accuracy of 95.05%, with LOS/NLOS recall rates of 95.72%/94.15%. The mitigated LOS errors are reduced by 50.4%, while the average improvement in the accuracy of the three types of NLOS ranging errors is 61.8%, reaching up to 76.84%. Overall, this method achieves a reduction in LOS and NLOS ranging errors of 25.19% and 69.85%, respectively, resulting in a 54.46% enhancement in positioning accuracy. This performance surpasses that of state-of-the-art techniques, such as the convolutional neural network (CNN), long short-term memory–extended Kalman filter (LSTM-EKF), least-squares–support vector machine (LS-SVM), and k-nearest neighbor (K-NN) algorithms.
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Lee, Juyul, Myung-Don Kim, Hyun Kyu Chung, and Jinup Kim. "NLOS Path Loss Model for Low-Height Antenna Links in High-Rise Urban Street Grid Environments." International Journal of Antennas and Propagation 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/651438.
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This paper presents a NLOS (non-line-of-sight) path loss model for low-height antenna links in rectangular street grids to account for typical D2D (device-to-device) communication link situations in high-rise urban outdoor environments. From wideband propagation channel measurements collected in Seoul City at 3.7 GHz, we observed distinctive power delay profile behaviors between 1-Turn and 2-Turn NLOS links: the 2-Turn NLOS has a wider delay spread. This can be explained by employing the idea that the 2-Turn NLOS has multiple propagation paths along the various street roads from TX to RX, whereas the 1-Turn NLOS has a single dominant propagation path from TX to RX. Considering this, we develop a path loss model encompassing 1-Turn and 2-Turn NLOS links with separate scattering and diffraction parameters for the first and the second corners, based on the Uniform Geometrical Theory of Diffraction. In addition, we consider the effect of building heights on path loss by incorporating an adjustable “waveguide effect” parameter; that is, higher building alleys provide better propagation environments. When compared with field measurements, the predictions are in agreement.
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Kan, Ruixiang, Mei Wang, Xin Liu, Xiaojuan Liu, and Hongbing Qiu. "An Advanced Artificial Fish School Algorithm to Update Decision Tree for NLOS Acoustic Localization Signal Identification with the Dual-Receiving Method." Applied Sciences 13, no. 6 (March 21, 2023): 4012. http://dx.doi.org/10.3390/app13064012.
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For indoor sensor systems, it is essential to implement an extra supporting area notification part. To inform the real-time coordinates, the time difference of arrival (TDOA) algorithm can be introduced. For these indoor localization systems, their main processes are often built based on the line of sight (LOS) scenario. However, obstacles make the off-the-shelf localization system unable to play its due role in the flexible non-line of sight (NLOS) scenario. So, it is necessary to adjust the signals according to the NLOS identification results. However, the NLOS identification methods before were not effective enough. To address these challenges, on the one hand, this paper proposes an adaptive strategy for a dual-receiving signal processing method. On the other hand, the system is matched with the homologous NLOS identification method based on a novel artificial fish school algorithm (AFSA) and the decision tree model. According to our experiments, our novel AFSA optimization method can obtain a better effect and take less time. The NLOS acoustic signal identification accuracy can be improved significantly in flexible scenarios compared with other methods. Based on these processes, the system will achieve more accurate localization results in flexible NLOS situations.
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Chen, Shiwa, Jianyun Zhang, Yunxiang Mao, Chengcheng Xu, and Yu Gu. "Efficient Distributed Method for NLOS Cooperative Localization in WSNs." Sensors 19, no. 5 (March 7, 2019): 1173. http://dx.doi.org/10.3390/s19051173.
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The accuracy of cooperative localization can be severely degraded in non-line-of-sight (NLOS) environments. Although most existing approaches modify models to alleviate NLOS impact, computational speed does not satisfy practical applications. In this paper, we propose a distributed cooperative localization method for wireless sensor networks (WSNs) in NLOS environments. The convex model in the proposed method is based on projection relaxation. This model was designed for situations where prior information on NLOS connections is unavailable. We developed an efficient decomposed formulation for the convex counterpart, and designed a parallel distributed algorithm based on the alternating direction method of multipliers (ADMM), which significantly improves computational speed. To accelerate the convergence rate of local updates, we approached the subproblems via the proximal algorithm and analyzed its computational complexity. Numerical simulation results demonstrate that our approach is superior in processing speed and accuracy to other methods in NLOS scenarios.
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Ganis, Laura, and Tatiana Christides. "Are We Neglecting Nutrition in UK Medical Training? A Quantitative Analysis of Nutrition-Related Education in Postgraduate Medical Training Curriculums." Nutrients 13, no. 3 (March 16, 2021): 957. http://dx.doi.org/10.3390/nu13030957.
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Suboptimal nutrition is a major cause of morbidity and mortality in the United Kingdom (UK). Although patients cite physicians as trusted information sources on diet and weight loss, studies suggest that the management of nutrition-related disorders is hindered by insufficient medical education and training. Objectives of this study were to: (1) Quantify nutrition-related learning objectives (NLOs) in UK postgraduate medical training curriculums and assess variation across specialties; (2) assess inclusion of nutrition-related modules; (3) assess the extent to which NLOs are knowledge-, skill-, or behaviour-based, and in which Good Medical Practice (GMP) Domain(s) they fall. 43 current postgraduate curriculums, approved by the General Medical Council (GMC) and representing a spectrum of patient-facing training pathways in the UK, were included. NLOs were identified using four keywords: ‘nutrition’, ‘diet’, ‘obesity’, and ‘lifestyle’. Where a keyword was used in a titled section followed by a number of objectives, this was designated as a ‘module’. Where possible, NLOs were coded with the information to address objective 3. A median of 15 NLOs (mean 24) were identified per curriculum. Eleven specialties (25.6%) had five or less NLOs identified, including General Practice. Surgical curriculums had a higher number of NLOs compared with medical (median 30 and 8.5, respectively), as well as a higher inclusion rate of nutrition-related modules (100% of curriculums versus 34.4%, respectively). 52.9% of NLOs were knowledge-based, 34.9% skill-based, and 12.2% behaviour-based. The most common GMP Domain assigned to NLOs was Domain 1: Knowledge, Skills and Performance (53.0%), followed by Domain 2: Safety and Quality (20.6%), 3: Communication, Partnership and Teamwork (18.7%), and 4: Maintaining Trust (7.7%). This study demonstrates considerable variability in the number of nutrition-related learning objectives in UK postgraduate medical training. As insufficient nutrition education and training may underlie inadequate doctor-patient discussions, the results of this analysis suggest a need for further evaluation of nutrition-related competencies in postgraduate training.
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Wu, Cheng, Jianjiang Liu, Xin Huang, Zheng-Ping Li, Chao Yu, Jun-Tian Ye, Jun Zhang, et al. "Non–line-of-sight imaging over 1.43 km." Proceedings of the National Academy of Sciences 118, no. 10 (March 3, 2021): e2024468118. http://dx.doi.org/10.1073/pnas.2024468118.
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Non–line-of-sight (NLOS) imaging has the ability to reconstruct hidden objects from indirect light paths that scatter multiple times in the surrounding environment, which is of considerable interest in a wide range of applications. Whereas conventional imaging involves direct line-of-sight light transport to recover the visible objects, NLOS imaging aims to reconstruct the hidden objects from the indirect light paths that scatter multiple times, typically using the information encoded in the time-of-flight of scattered photons. Despite recent advances, NLOS imaging has remained at short-range realizations, limited by the heavy loss and the spatial mixing due to the multiple diffuse reflections. Here, both experimental and conceptual innovations yield hardware and software solutions to increase the standoff distance of NLOS imaging from meter to kilometer range, which is about three orders of magnitude longer than previous experiments. In hardware, we develop a high-efficiency, low-noise NLOS imaging system at near-infrared wavelength based on a dual-telescope confocal optical design. In software, we adopt a convex optimizer, equipped with a tailored spatial–temporal kernel expressed using three-dimensional matrix, to mitigate the effect of the spatial–temporal broadening over long standoffs. Together, these enable our demonstration of NLOS imaging and real-time tracking of hidden objects over a distance of 1.43 km. The results will open venues for the development of NLOS imaging techniques and relevant applications to real-world conditions.
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Si, Minghao, Yunjia Wang, Shenglei Xu, Meng Sun, and Hongji Cao. "A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification." Applied Sciences 10, no. 3 (February 2, 2020): 956. http://dx.doi.org/10.3390/app10030956.
Full textAbstract:
In recent years, many new technologies have been used in indoor positioning. In 2016, IEEE 802.11-2016 created a Wi-Fi fine timing measurement (FTM) protocol, making Wi-Fi ranging more robust and accurate, and providing meter-level positioning accuracy. However, the accuracy of positioning methods based on the new ranging technology is influenced by non-line-of-sight (NLOS) errors. To enhance the accuracy, a positioning method with LOS (line-of-sight)/NLOS identification is proposed in this paper. A Gaussian model has been established to identify NLOS signals. After identifying and discarding NLOS signals, the least square (LS) algorithm is used to calculate the location. The results of the numerical experiments indicate that our algorithm can identify and discard NLOS signals with a precision of 83.01% and a recall of 74.97%. Moreover, compared with the traditional algorithms, by all ranging results, the proposed method features more accurate and stable results for indoor positioning.
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Yang, Yufeng, Kailei Yang, and Ao Zhang. "Influence of Target Surface BRDF on Non-Line-of-Sight Imaging." Journal of Imaging 10, no. 11 (October 29, 2024): 273. http://dx.doi.org/10.3390/jimaging10110273.
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The surface material of an object is a key factor that affects non-line-of-sight (NLOS) imaging. In this paper, we introduce the bidirectional reflectance distribution function (BRDF) into NLOS imaging to study how the target surface material influences the quality of NLOS images. First, the BRDF of two surface materials (aluminized insulation material and white paint board) was modeled using deep neural networks and compared with a five-parameter empirical model to validate the method’s accuracy. The method was then applied to fit BRDF data for different common materials. Finally, NLOS target simulations with varying surface materials were reconstructed using the confocal diffusion tomography algorithm. The reconstructed NLOS images were classified via a convolutional neural network to assess how different surface materials impacted imaging quality. The results show that image clarity improves when decreasing the specular reflection and increasing the diffuse reflection, with the best results obtained for surfaces exhibiting a high diffuse reflection and no specular reflection.
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Chen, Xiaojie, Mengyue Li, Tiantian Chen, and Shuyue Zhan. "Long-Range Non-Line-of-Sight Imaging Based on Projected Images from Multiple Light Fields." Photonics 10, no. 1 (December 26, 2022): 25. http://dx.doi.org/10.3390/photonics10010025.
Full textAbstract:
Non-line-of-sight (NLOS) imaging technology has shown potential in several applications, such as intelligent driving, warfare and reconnaissance, medical diagnosis, and disaster rescue. However, most NLOS imaging systems are expensive and have a limited detection range, which hinders their utility in real-world scenarios. To address these limitations, we designed an NLOS imaging system, which is capable of long-range data acquisition. We also introduce an NLOS object imaging method based on deep learning, which makes use of long-range projected images from different light fields to reconstruct hidden objects. The method learns the mapping relationships of projected images and objects and corrects the image structure to suppress the generation of artifacts in order to improve the reconstruction quality. The results show that the proposed method produces fewer artifacts in reconstructions, which are close to human subjective perception. Furthermore, NLOS targets can be reconstructed even if the distance between the detection device and the intermediate surface exceeds 50 m.
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KOLA, Ahmet Furkan, and Çetin KURNAZ. "Analysis of MIMO Channel Capacity at 28/73 GHz with NYUSIM Channel Simulator." Uluslararası Muhendislik Arastirma ve Gelistirme Dergisi 15, no. 1 (January 31, 2023): 211–17. http://dx.doi.org/10.29137/umagd.1132069.
Full textAbstract:
Analyzing the channel models in the mm-wave bandwidth is critical for 5G system performance. This study investigated the effects of 28 GHz and 73 GHz frequencies, the number of transmitting and receiving antennas, and LOS/NLOS parameters on 5G channel capacity using the NYUSIM channel simulator. As a result of the analysis, changing from a 2x2 to a 64x64 antenna structure for 28 GHz increased capacity by 29.78 times for LOS and 26.91 times for NLOS. When changing the MIMO configuration from 2x2 to 64x64 at 73 GHz, the channel capacity rises 36.88 times for LOS and 29.00 times for NLOS. With a 64x64 antenna structure, the channel capacity for 28 GHz and LOS is 8.81 times higher than for 73 GHz, and it is 12.56 times higher for NLOS. For the 28 GHz 64x64 structure and LOS condition, the channel capacity is 215.69 times higher than the NLOS condition, while this value is 307.7 times for 73 GHz.
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Bian, Zhentian, Long Cheng, and Yan Wang. "A Multifilter Location Optimization Algorithm Based on Neural Network in LOS/NLOS Mixed Environment." Journal of Sensors 2021 (November 13, 2021): 1–15. http://dx.doi.org/10.1155/2021/6125890.
Full textAbstract:
While the modern communication system, embedded system, and sensor technology have been widely used at the moment, the wireless sensor network (WSN) composed of microdistributed sensors is favored due to its relatively excellent communication interaction, real-time computing, and sensing capabilities. Because GPS positioning technology cannot meet the needs of indoor positioning, positioning based on WSN has become the better option for indoor localization. In the field of WSN indoor positioning, how to cope with the impact of NLOS error on positioning is still a big problem to be solved. In order to mitigate the influence of NLOS errors, a Neural Network Modified Multiple Filter Localization (NNMML) algorithm is proposed in this paper. In this algorithm, LOS and NLOS cases are distinguished firstly. Then, KF and UKF are applied in the LOS case and the NLOS case, respectively, and appropriate grouping processing is carried out for NLOS data. Finally, the positioning results after multiple filtering are corrected by neural network. The simulation results illustrate that the location accuracy of NNMML algorithm is better than that of KF, EKF, UKF, and the version without neural network correction. It also shows that NNMML is suitable for the situation with large NLOS error.
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Hua, Jingyu, Yejia Yin, Weidang Lu, Yu Zhang, and Feng Li. "NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks." Sensors 18, no. 9 (September 7, 2018): 2991. http://dx.doi.org/10.3390/s18092991.
Full textAbstract:
The problem of target localization in WSN (wireless sensor network) has received much attention in recent years. However, the performance of traditional localization algorithms will drastically degrade in the non-line of sight (NLOS) environment. Moreover, variable methods have been presented to address this issue, such as the optimization-based method and the NLOS modeling method. The former produces a higher complexity and the latter is sensitive to the propagating environment. Therefore, this paper puts forward a simple NLOS identification and localization algorithm based on the residual analysis, where at least two line-of-sight (LOS) propagating anchor nodes (AN) are required. First, all ANs are grouped into several subgroups, and each subgroup can get intermediate position estimates of target node through traditional localization algorithms. Then, the AN with an NLOS propagation, namely NLOS-AN, can be identified by the threshold based hypothesis test, where the test variable, i.e., the localization residual, is computed according to the intermediate position estimations. Finally, the position of target node can be estimated by only using ANs under line of sight (LOS) propagations. Simulation results show that the proposed algorithm can successfully identify the NLOS-AN, by which the following localization produces high accuracy so long as there are no less than two LOS-ANs.