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Journal articles on the topic 'NLOS'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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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 (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. Therefor

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2

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 (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 NLO

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3

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 (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 dela

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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 (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 prop

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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 ext

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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 pin

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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

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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 conta

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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 (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) ident

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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 (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

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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 (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

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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 (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 tra

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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 o

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Suzuki, Taro, and Yoshiharu Amano. "NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning." Sensors 21, no. 7 (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

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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 me

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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 (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 cr

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Wang, Lei, Ruizhi Chen, Lili Shen, et al. "NLOS Mitigation in Sparse Anchor Environments with the Misclosure Check Algorithm." Remote Sensing 11, no. 7 (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 NLO

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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 (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 pseudor

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Zheng, Xingyu, Ruizhi Chen, Liang Chen, et al. "A Novel Device-Free Positioning Method Based on Wi-Fi CSI with NLOS Detection and Bayes Classification." Remote Sensing 15, no. 10 (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

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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 featur

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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 (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 ar

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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 (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 w

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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) base

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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 (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

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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 fitt

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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 ac

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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 (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, w

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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 (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 functio

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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. Base

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Chen, Jiajing, Xuefeng Yin, Li Tian, et al. "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 ar

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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

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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 (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

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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 (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 communic

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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 (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

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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 (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 te

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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 (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 sa

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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 (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 informat

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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 (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 reca

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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, wher

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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 (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

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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 (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

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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 (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-

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Wu, Cheng, Jianjiang Liu, Xin Huang, et al. "Non–line-of-sight imaging over 1.43 km." Proceedings of the National Academy of Sciences 118, no. 10 (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

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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 (2020): 956. http://dx.doi.org/10.3390/app10030956.

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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

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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 (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

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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 (2022): 25. http://dx.doi.org/10.3390/photonics10010025.

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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

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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 (2023): 211–17. http://dx.doi.org/10.29137/umagd.1132069.

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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

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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.

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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 o

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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 (2018): 2991. http://dx.doi.org/10.3390/s18092991.

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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 an

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