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Letteratura scientifica selezionata sul tema "NLOS"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 25 luglio 2025

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Articoli di riviste sul tema "NLOS"

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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Abstract (sommario):

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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4

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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5

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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6

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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Abstract (sommario):

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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7

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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Abstract (sommario):

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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8

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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Abstract (sommario):

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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9

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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Abstract (sommario):

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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10

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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Abstract (sommario):

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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Tesi sul tema "NLOS"

1

Ko, Edwin Hoi-Kit. "NLOS mobile location determination in CDMA cellular systems." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0001/MQ45990.pdf.

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2

Al-Jazzar, Saleh. "Algorithms and Parameter Estimation for Radiolocation in NLOS Environments." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1084913001.

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3

Tau, Sieskul Bamrung [Verfasser]. "NLoS Localization and UWB Channel Capacity Analysis / Bamrung Tau Sieskul." Aachen : Shaker, 2010. http://d-nb.info/1080766995/34.

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4

Kbayer, Nabil. "Advanced Signal Processing Methods for GNSS Positioning with NLOS/Multipath Signals." Thesis, Toulouse, ISAE, 2018. http://www.theses.fr/2018ESAE0017/document.

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Abstract (sommario):

Les avancées récentes dans le domaine de navigation par satellites (GNSS) ontconduit à une prolifération des applications de géolocalisation dans les milieux urbains. Pourde tels environnements, les applications GNSS souffrent d’une grande dégradation liée à laréception des signaux satellitaires en lignes indirectes (NLOS) et en multitrajets (MP). Cetravail de thèse propose une méthodologie originale pour l’utilisation constructive des signauxdégradés MP/NLOS, en appliquant des techniques avancées de traitement du signal ou àl’aide d’une assistance d’un simulateur 3D de propagation des signaux

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5

Dammes, Timo [Verfasser]. "Probabilistic Range Estimation for Wireless Indoor Localization in NLOS Scenarios / Timo Dammes." Aachen : Shaker, 2015. http://d-nb.info/1069050407/34.

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6

Li, Hao. "New mobile positioning techniques for LOS/NLOS environments and investigation of topology influence." Thesis, University of Birmingham, 2015. http://etheses.bham.ac.uk//id/eprint/6007/.

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Abstract (sommario):

The advent of wireless location technology and the increase in location-based services, has meant the need to investigate efficient network-based location methods becoming of paramount importance. Therefore, the interest in wireless positioning techniques has been increasing over recent decades. Among mobile positioning techniques, the Time of Arrival (TOA) and Time Difference of Arrival (TDOA) look promising. For the purpose of dealing with such technologies, some classic algorithms such as least square, most likelihood and Taylor method have been used to solve the estimation, which distingui

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7

Lyu, Pengfei. "Numerical and Experimental Indoor Channel Analysis for LOS-NLOS Identification at 60 GHz." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS186.

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Avec l’émergence des objets connectés, les applications potentielles requérant la localisation indoor ou la continuité de localisation réapparaissent et suscitent, depuis quelques années déjà, de nombreuses études et recherches. La norme IEEE 802.11ad, opérant autour de 60 GHz, est un candidat potentiel pour résoudre le problème de localisation tout en permettant une communication très haut débit. Cette norme prometteuse pose toutefois un problème lié au caractère directionnel de la propagation, particulièrement sujette au blocage des ondes directes. En effet aucun calcul de coordonnées spatia

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8

Li, Binghao Surveying &amp Spatial Information Systems Faculty of Engineering UNSW. "Terrestial mobile user positioning using TDOA and fingerprinting techniques." Awarded by:University of New South Wales. School of Surveying and Spatial Information Systems, 2006. http://handle.unsw.edu.au/1959.4/25507.

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Specialists are expecting the knowledge of location will trigger yet another revolution in mobile services. Location-base services (LBS) have attracted many researchers and enterprises and one of the key aspects of LBS is positioning technology. Considering a wider and more complex field - ubiquitous computing, location is the fundamental element. This work focuses on some aspects of the new techniques of terrestrial positioning systems. Wireless LAN is one of the most popular systems used for positioning for indoor environments and public places. We have investigated the trilateration and fi

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9

Venkatraman, Saipradeep. "Wireless Location in Non-Line-of-Sight Environments." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1085685908.

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10

Olayanju, Iyeyinka Damilola, and Olabode Paul Ojelabi. "Using Multilateration and Extended Kalman Filter for Localization of RFID Passive Tag in NLOS." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-1119.

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The use of ubiquitous network has made real time tracking of objects, animals and human beings easy through the use of radio frequency identification system (RFID). Localization techniques in RFID rely on accurate estimation of the read range between the reader and the tags. The tags consist of a small chip and a printed antenna which receives from and transmits information to the reader. The range information about the distance between the tag and the reader is obtained from the received signal strength indication (RSSI). Accuracy of the read range using RSSI can be very complicated especiall

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Libri sul tema "NLOS"

1

Ko, Edwin Hoi-Kit. NLOS mobile location determination in CDMA cellular systems. National Library of Canada, 1999.

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2

McCoy, Edward D. Employment and command and control for the Non-Line-of-Sight (NLOS) missile system. Naval Postgraduate School, 1990.

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3

Kozyrev, Vi︠a︡cheslav. Rukotvornye NLO. "I︠A︡UZA", 2005.

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4

Zubchenko, Mikhail. Piloty NLO. Putʹ, 1994.

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5

Azhazha, V. G. Podvodnye NLO. Veche, 2008.

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6

Gershteĭn, Mikhail. Taĭny krusheniĭ NLO. AST, 2007.

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7

Gershteĭn, Mikhail. Taĭny krusheniĭ NLO. AST, 2007.

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8

I͡Akubovskiĭ, Ėduard. NLO vokrug nas. Sredne-Uralʹskoe knizhnoe izd-vo, 1992.

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9

Chernobrov, V. A. Ėnt︠s︡iklopedii︠a︡ vizitov NLO. Veche, 2008.

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10

V, Rubt͡sov V., and Novikov V. D. 1939-, eds. NLO i sovremennai͡a nauka. "Nauka", 1991.

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Capitoli di libri sul tema "NLOS"

1

Liu, Deliang, Yi Yao, and You Zhai. "TOA Localization in NLOS Environments." In Lecture Notes in Electrical Engineering. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3229-5_52.

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2

Gentile, Camillo, Nayef Alsindi, Ronald Raulefs, and Carole Teolis. "Multipath and NLOS Mitigation Algorithms." In Geolocation Techniques. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-1836-8_3.

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3

Sharp, Ian, and Kegen Yu. "NLOS Mitigation for Vehicle Tracking." In Navigation: Science and Technology. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8791-2_16.

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4

Lie, Joni Polili, Chin-Heng Lim, and Chong-Meng Samson See. "NLOS Mitigation Methods for Geolocation." In Handbook of Position Location. John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118104750.ch17.

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5

Tian, Qiang, Yaobo Liu, and Qi Hu. "A NLOS Mitigation Algorithm for TOA Based Localization in Mixed LOS/NLOS Environments." In Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022). Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0479-2_263.

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6

Zhu, Xuanzhang. "A Novel NLOS Mitigation Algorithm Using VBS." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40633-1_27.

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7

Xu, Wenjie, Zhonghai Wang, and Seyed A. Reza Zekavat. "An Introduction to NLOS Identification and Localization." In Handbook of Position Location. John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118104750.ch16.

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8

Kaiser, Thomas, Christiane Senger, Amr Eltaher, and Bamrung Tau Sieskul. "Localisation in NLOS Scenarios with UWB Antenna Arrays." In Ultra-Wideband. John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470056843.ch18.

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9

Bao, Dazhi, Hao Zhou, Hao Chen, Shaojie Liu, Yifan Zhang, and Zhiyong Feng. "TOA Based Localization Under NLOS in Cognitive Radio Network." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40352-6_55.

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10

Sun, Yinghao, Gang Wang, and Youming Li. "Robust RSS-Based Localization in Mixed LOS/NLOS Environments." In Communications and Networking. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41114-5_49.

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Atti di convegni sul tema "NLOS"

1

Lotti, Marina, Giacomo Calesini, and Davide Dardari. "NLOS Localization Exploiting Frequency-selective Metasurfaces." In 2024 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, 2024. http://dx.doi.org/10.1109/iccworkshops59551.2024.10615308.

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2

Hu, Baojun, Xin Li, and Xiaodai Dong. "Learning-Based Ultra-Wideband Indoor NLOS Identification." In 2024 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM). IEEE, 2024. http://dx.doi.org/10.1109/pacrim61180.2024.10690195.

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3

Du, Jinze, and Hongchen Zhang. "NLOS recognition based on CSI statistical features." In 2024 4th International Symposium on Computer Technology and Information Science (ISCTIS). IEEE, 2024. http://dx.doi.org/10.1109/isctis63324.2024.10698795.

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4

Abbas, Ali Alhaj, Aman Batra, Yamen Zantah, Fawad Sheikh, and Thomas Kaiser. "THz SAR Assisted NLoS Passive Tag Identification." In 2025 International Conference on Mobile and Miniaturized Terahertz Systems (ICMMTS). IEEE, 2025. https://doi.org/10.1109/icmmts62835.2025.10926046.

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5

Hayashi, Ryuga, and Yukihiro Kubo. "NLOS Satellites Detection Method Applying Relative Positioning." In TENCON 2024 - 2024 IEEE Region 10 Conference (TENCON). IEEE, 2024. https://doi.org/10.1109/tencon61640.2024.10903047.

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6

Zhou, Yuxuan, Tianjiao Yang, and Dengke He. "NLOS Localization Method Based on Kirchhoff Migration." In 2025 6th International Conference on Geology, Mapping and Remote Sensing (ICGMRS). IEEE, 2025. https://doi.org/10.1109/icgmrs66001.2025.11065141.

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7

Sorrentino, A., F. Nunziata, G. Ferrara, and M. Migliaccio. "An effective indicator for NLOS, nLOS, LOS propagation channels conditions." In 2012 6th European Conference on Antennas and Propagation (EuCAP). IEEE, 2012. http://dx.doi.org/10.1109/eucap.2012.6206269.

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8

Bocquet, M., Ch Loyez, and A. Benlarbi-Delai. "Weak NLOS Channel Sounding." In 2006 European Microwave Conference. IEEE, 2006. http://dx.doi.org/10.1109/eumc.2006.280999.

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9

Shin, Ui-Hyeon, Seungwoo Jang, and Kwangsu Kim. "Acoustic NLOS Imaging with Cross Modal Knowledge Distillation." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/156.

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Acoustic non-line-of-sight (NLOS) imaging aims to reconstruct hidden scenes by analyzing reflections of acoustic waves. Despite recent developments in the field, existing methods still have limitations such as sensitivity to noise in a physical model and difficulty in reconstructing unseen objects in a deep learning model. To address these limitations, we propose a novel cross-modal knowledge distillation (CMKD) approach for acoustic NLOS imaging. Our method transfers knowledge from a well-trained image network to an audio network, effectively combining the strengths of both modalities. As a r

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10

Triki, Mahdi, and Dirk T. M. Slock. "Mobile Localization for NLOS Propagation." In 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications. IEEE, 2007. http://dx.doi.org/10.1109/pimrc.2007.4394714.

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Rapporti di organizzazioni sul tema "NLOS"

1

Hemenway, Mark, and Ronald H. Lafond. Non-Line of Sight-Combined Arms (NLOS-CA) Manpower, Personnel and Logistics Impact Analysis (LIA). Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada274201.

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2

Emis, Jonathon, Bryan Huang, Timothy Jones, Mei Li, and Don Tumbocon. Integrating the Non-Line of Sight Launching System (NLOS-LS) in the United States Navy. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada465615.

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3

Hemenway, Mark, and Ronald H. Lafond. Non-Line of Sight - Combined Army (NLOS-CA) Manpower, Personnel and Logistics Impact Analyses (LIA) - Abstract. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada274164.

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4

Alfano, James, Isaac Barash, Thomas Clemente, Paul E. Staswick, Guido Sessa, and Shulamit Manulis. Elucidating the Functions of Type III Effectors from Necrogenic and Tumorigenic Bacterial Pathogens. United States Department of Agriculture, 2010. http://dx.doi.org/10.32747/2010.7592638.bard.

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Many phytopathogenic bacteria use a type III protein secretion system (T3SS) to inject type III effectors into plant cells. In the experiments supported by this one-year feasibility study we investigated type III effector function in plants by using two contrasting bacterial pathogens: Pseudomonas syringae pv. tomato, a necrotrophic pathogen and Pantoea agglomerans, a tumorigenic pathogen. The objectives are listed below along with our major conclusions, achievements, and implications for science and agriculture. Objective 1: Compare Pseudomonas syringae and Pantoea agglomerans type III effect

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5

Pack, D. J., and D. J. Downing. NLO error propagation exercise: statistical results. Office of Scientific and Technical Information (OSTI), 1985. http://dx.doi.org/10.2172/5053984.

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6

Soni, Som R., J. B. Skidmore, Rajesh Tiwari, R. M. McKellar, and Perry P. Yancey. Nonlinear Optical (NLO) Materials and Device Development. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada380784.

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7

Brandelik, Joseph. Nonlinear Optical (NLO) Polymer Opto-Electronic Devices. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada384482.

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8

Anastasiou, Charalampos. NLO Higgs boson rapidity distributions at hadron colliders. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/808704.

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9

Mandal, B. K., T. Takahsshi, M. Maeda, S. Kumar, and A. Blumstein. Comb-Like Polymers Containing NLO active Pendant Groups. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada226514.

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Wright, Michael E., and Edward G. Toplikar. Organometallic Nonlinear Optical (NLO) Polymers. 2. Synthesis of Main-Chain Organometallic Polymers and a Structural Study of Ferrocene NLO-Phores. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada250655.

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