Open Access
Issue |
JNWPU
Volume 40, Number 4, August 2022
|
|
---|---|---|
Page(s) | 865 - 874 | |
DOI | https://doi.org/10.1051/jnwpu/20224040865 | |
Published online | 30 September 2022 |
- FERREIRA A G, FERNANDES D, BRANCO S, et al. Feature selection for real-time nlos identification and mitigation for body-mounted uwb transceivers[J]. IEEE Trans on Instrumentation and Measurement, 2021, 70(1): 1–10 [CrossRef] [Google Scholar]
- YU K, WEN K, LI Y, et al. A novel NLOS mitigation algorithm for UWB localization in harsh indoor environments[J]. IEEE Trans on Vehicular Technology, 2019, 68(1): 686–699. [Article] [CrossRef] [Google Scholar]
- WU S, ZHANG S, HUANG D. A TOA-based localization algorithm with simultaneous nlos mitigation and synchronization error elimination[J]. IEEE Sensors Letters, 2019, 3(3): 1–4 [Google Scholar]
- KATWE M, GHARE P, SHARMA P K, et al. NLOS error mitigation in hybrid RSS-TOA-based localization through semi-definite relaxation[J]. IEEE Communications Letters, 2020, 24(12): 2761–2765. [Article] [CrossRef] [Google Scholar]
- XIAO Z, WEN H, MARKHAM A, et al. Non-line-of-sight Identification and mitigation using received signal strength[J]. IEEE Trans on Wireless Communications, 2015, 14(3): 1689–1702. [Article] [CrossRef] [Google Scholar]
- WU C, HOU H, WANG W, et al. TDOA based indoor positioning with nlos identification by machine learning[C]//2018 10th International Conference on Wireless Communications and Signal Processing, 2018: 1–6 [Google Scholar]
- CHITAMBIRA B, ARMOUR S, WALES S, et al. Direct localisation using ray-tracing and least-squares support vector machines[C]//8th International Conference on Localization and GNSS, 2018: 1–5 [Google Scholar]
- LI S, SONG B, LUO K. NLOS mitigation for UWB localization based on machine learning fusion method[C]//2019 IEEE Symposium Series on Computational Intelligence, 2019: 1048–1055 [CrossRef] [Google Scholar]
- YANG X F. NLOS mitigation for UWB localization based on sparse pseudo-input gaussian process[J]. IEEE Sensors Journal, 2018, 18(10): 4311–4316. [Article] [CrossRef] [Google Scholar]
- SILVA B, HANCKE G P. Ranging error mitigation for through-the-wall non-line-of-sight conditions[J]. IEEE Trans on Industrial Informatics, 2020, 16(11): 6903–6911. [Article] [CrossRef] [Google Scholar]
- TIAN Q, WANG K I, SALCIC Z. Human body shadowing effect on UWB-Based ranging system for pedestrian tracking[J]. IEEE Trans on Instrumentation and Measurement, 2019, 68(10): 4028–4037. [Article] [CrossRef] [Google Scholar]
- CHITAMBIRA B, ARMOUR S, WALES S, et al. NLOS identification and mitigation for geolocation using least-squares support vector machines[C]//2017 IEEE Wireless Communications and Networking Conference, San Francisco, 2017: 1–6 [Google Scholar]
- TIAN Chunyuan, YU Jiang, CHANG Jun, et al. NWI: CSI-based non-line-of-sight signal identification method[J]. Computer Science, 2020, 47(11): 327–332. [Article] (in Chinese) [Google Scholar]
- CHOI J S, LEE W H, LEE J H, et al. Deep learning based NLOS identification with commodity WLAN devices[J]. IEEE Trans on Vehicular Technology, 2018, 67(4): 3295–3303. [Article] [CrossRef] [Google Scholar]
- ZENG H, XIE R, XU R, et al. A novel approach to NLOS identification for UWB positioning based on kernel learning[C]//2019 IEEE 19th International Conference on Communication Technology, Xi'an, 2019: 451–455 [Google Scholar]
- NAM S C, CHOI H B, KO Y B. On mitigation of ranging errors for through-the-body NLOS conditions using convolutional neural networks[C]//2021 23rd International Conference on Advanced Communication Technology, 2021: 141–144 [Google Scholar]
- DONG M Y. A low-cost NLOS identification and mitigation method for UWB ranging in static and dynamic environments[J]. IEEE Communications Letters, 2021, 1(1): 2420–2424 [CrossRef] [Google Scholar]
- CHITAMBIRA B, ARMOUR S, WALES S, et al. NLOS identification and mitigation for geolocation using least-squares support vector machines[C]//IEEE Wireless Communications and Networking Conference, 2017: 1–6 [Google Scholar]
- MUSA A, NUGRAHA G D, HAN H, et al. A decision tree-based NLOS detection method for the UWB indoor location tracking accuracy improvement[J]. International Journal of Communication Systems, 2019, 32(13): 1–13 [Google Scholar]
- CAO Y, ZHANG L H, Data fusion of heterogeneous network based on BP neural network and improved SEP[C]//2017 9th International Conference on Advanced Infocomm Technology, Chengdu, 2017: 138–142 [Google Scholar]
- BREGAR K, MOHORCIC M. Improving indoor localization using convolutional neural networks on computationally restricted devices[J]. IEEE Access, 2018, 6(1): 17429–17441 [CrossRef] [Google Scholar]
- SILVA B, HANCKE G P. Ranging error mitigation for through-the-wall non-line-of-sight conditions[J]. IEEE Trans on Industrial Informatics, 2020, 16(11): 6903–6911. [Article] [CrossRef] [Google Scholar]
- SRIDHAR B, ALI KHAN M Z. RMSE comparison of path loss models for UHF/VHF bands in India[C]//2014 IEEE Region 10 Symposium, Kuala Lumpur, 2014: 330–335 [CrossRef] [Google Scholar]
- WANG T, HU K K, LI Z H, et al. A semi-supervised learning approach for UWB ranging error mitigation[J]. IEEE Wireless Communications Letters, 2021, 10(3): 688–691. [Article] [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.