PMID- 38475239
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240315
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 24
IP  - 5
DP  - 2024 Mar 6
TI  - Ultra-Wideband Ranging Error Mitigation with Novel Channel Impulse Response 
      Feature Parameters and Two-Step Non-Line-of-Sight Identification.
LID - 10.3390/s24051703 [doi]
LID - 1703
AB  - 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.
FAU - Yang, Hongchao
AU  - Yang H
AUID- ORCID: 0000-0002-0848-8632
AD  - The Key Laboratory of Land Environment and Disaster Monitoring, China University 
      of Mining and Technology, Xuzhou 221116, China.
FAU - Wang, Yunjia
AU  - Wang Y
AUID- ORCID: 0000-0002-8612-3305
AD  - The Key Laboratory of Land Environment and Disaster Monitoring, China University 
      of Mining and Technology, Xuzhou 221116, China.
FAU - Xu, Shenglei
AU  - Xu S
AUID- ORCID: 0000-0001-7141-3580
AD  - The Navigation Institute of Jimei University, Xiamen 361021, China.
FAU - Bi, Jingxue
AU  - Bi J
AUID- ORCID: 0000-0002-2964-7698
AD  - The School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 
      250101, China.
FAU - Jia, Haonan
AU  - Jia H
AUID- ORCID: 0000-0003-3502-1453
AD  - The School of Instrument Science and Engineering, Southeast University, Nanjing 
      210096, China.
FAU - Seow, Cheekiat
AU  - Seow C
AUID- ORCID: 0000-0002-6499-9410
AD  - The School of Computing Science, University of Glasgow, Glasgow G12 8RZ, UK.
LA  - eng
GR  - No.016YFB0502102/Ministry of Science and Technology of the People's Republic of 
      China/
GR  - No.42001397/Ministry of Science and Technology of the People's Republic of China/
PT  - Journal Article
DEP - 20240306
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
PMC - PMC10934496
OTO - NOTNLM
OT  - channel impulse response (CIR)
OT  - deep learning
OT  - indoor positioning and navigation
OT  - non-line of sight (NLOS)
OT  - ranging mitigation
OT  - ultra-wideband (UWB)
COIS- The authors declare no conflicts of interest.
EDAT- 2024/03/13 06:46
MHDA- 2024/03/13 06:47
PMCR- 2024/03/06
CRDT- 2024/03/13 01:31
PHST- 2023/12/26 00:00 [received]
PHST- 2024/02/17 00:00 [revised]
PHST- 2024/02/18 00:00 [accepted]
PHST- 2024/03/13 06:47 [medline]
PHST- 2024/03/13 06:46 [pubmed]
PHST- 2024/03/13 01:31 [entrez]
PHST- 2024/03/06 00:00 [pmc-release]
AID - s24051703 [pii]
AID - sensors-24-01703 [pii]
AID - 10.3390/s24051703 [doi]
PST - epublish
SO  - Sensors (Basel). 2024 Mar 6;24(5):1703. doi: 10.3390/s24051703.