一种行人遮挡下的UWB非视距传播识别方法

doi:10.11959/j.issn.2096-3750.2023.00348

Abstract

Abstract:

Ultrawideband (UWB) is a hot technology for indoor positioning with large bandwidth, strong anti-interference ability, and high multipath resolution capacity.However, due to the complex indoor environment, UWB signal propagation will inevitably be blocked, resulting in non-line-of-sight (NLOS) propagation, which greatly reduces the accuracy of UWB positioning.Therefore, identifying NLOS signals accurately and discarding or correcting them are important to alleviate the problem of the decline in positioning accuracy.The majority of present NLOS identification work focuses on scenes with building structures such as walls.Further discussion is needed for scenes obscured by pedestrians.Since the impact of human obstacles on the signals is more complex and cannot be ignored, the NLOS identification under pedestrian occlusion was studied.By comparing a variety of machine learning methods and signal feature combinations, the random forest method based on the three-dimensional features of the first path signal power, the received signal power, and the measured distance was proposed.These features with fewer dimensions and easy extraction were used to achieve a high identification percentage for NLOS.The experimental results based on the measured data of different devices show that the NLOS identification accuracy based on the proposed method reaches 99.05%, 99.32% and 98.81% respectively.

Key words: UWB, indoor positioning, NLOS identification, random forest

CLC Number:

TN91

Tong WU, Yeshen LI, Zhenhuang HUANG, Yu ZHANG, Wanle ZHANG, Ke XIONG. A UWB NLOS identification method under pedestrian occlusion[J]. Chinese Journal on Internet of Things, 2023, 7(4): 63-71.

Figures/Tables 13

特征	naive Bayes		SVM		KNN		FNN		RF
特征	准确率	召回率	准确率	召回率	准确率	召回率	准确率	召回率	准确率	召回率
Md	53.78%	40.44%	54.82%	50.85%	91.14%	85.45%	86.00%	73.37%	90.94%	84.93%
FP、RX	76.98%	80.31%	76.73%	80.43%	79.46%	80.13%	79.53%	79.98%	79.47%	78.69%
Md、FP	76.69%	80.55%	85.20%	82.56%	98.80%	97.93%	98.33%	97.35%	98.72%	98.19%
Md、FP、RX	76.07%	81.58%	85.88%	80.37%	98.92%	98.10%	98.34%	97.40%	$\underline{99.05 %}$	$\underline{98.54 %}$

特征	naive Bayes		SVM		KNN		FNN		RF
特征	准确率	召回率	准确率	召回率	准确率	召回率	准确率	召回率	准确率	召回率
Md	56.10%	50.38%	54.77%	55.60%	90.20%	83.95%	88.33%	79.41%	90.22%	84.40%
FP、RX	70.58%	67.15%	70.90%	61.86%	77.02%	76.65%	77.04%	78.32%	77.07%	78.32%
Md、FP	70.28%	69.17%	89.28%	84.68%	99.24%	98.82%	97.85%	96.34%	99.21%	98.84%
Md、FP、RX	70.45%	69.51%	89.59%	84.57%	99.26%	98.78%	98.43%	97.38%	$\underline{99.32} %$	$\underline{98.94} %$

特征	naive Bayes		SVM		KNN		FNN		RF
特征	准确率	召回率	准确率	召回率	准确率	召回率	准确率	召回率	准确率	召回率
Md	54.52%	63.89%	53.01%	81.14%	94.15%	90.14%	93.33%	89.57%	93.92%	90.31%
FP、RX	67.90%	63.10%	82.41%	73.58%	87.26%	80.55%	86.44%	79.10%	84.70%	82.32%
Md、FP	57.87%	43.89%	72.32%	75.52%	96.32%	94.02%	88.65%	86.82%	96.23%	95.36%
Md、FP、RX	66.74%	63.35%	86.05%	81.04%	98.23%	97.27%	94.46%	91.65%	$\underline{98.81} %$	$\underline{98.54} %$

References 36

[1]	BIANCHI V , CIAMPOLINI P , DE M I . RSSI-based indoor localization and identification for ZigBee wireless sensor networks in smart homes[J]. IEEE Transactions on Instrumentation and Measurement, 2019,68(2): 566-575.
[2]	MAINETTI L , PATRONO L , SERGI I . A survey on indoor positioning systems[C]// Proceedings of 2014 22nd International Conference on Software,Telecommunications and Computer Networks (SoftCOM). Piscataway:IEEE Press, 2015: 111-120.
[3]	ALARIFI A , AL-SALMAN A , ALSALEH M ,et al. Ultra wideband indoor positioning technologies:analysis and recent advances[J]. Sensors (Basel,Switzerland), 2016,16(5): 707.
[4]	FONTANA R J . Recent system applications of short-pulse ultra-wideband (UWB) technology[J]. IEEE Transactions on Microwave Theory and Techniques, 2004,52(9): 2087-2104.
[5]	ZAFARI F , GKELIAS A , LEUNG K K . A survey of indoor localization systems and technologies[J]. IEEE Communications Surveys ＆Tutorials, 2019,21(3): 2568-2599.
[6]	SOGANCI H , GEZICI S , POOR H V . Accurate positioning in ultra-wideband systems[J]. IEEE Wireless Communications, 2011,18(2): 19-27.
[7]	LIU H , DARABI H , BANERJEE P ,et al. Survey of wireless indoor positioning techniques and systems[J]. IEEE Transactions on Systems,Man,and Cybernetics,Part C (Applications and Reviews), 2007,37(6): 1067-1080.
[8]	VENKATESH S , BUEHRER R M . NLOS mitigation using linear programming in ultrawideband location-aware networks[J]. IEEE Transactions on Vehicular Technology, 2007,56(5): 3182-3198.
[9]	KHODJAEV J , PARK Y , MALIK A S . Survey of NLOS identification and error mitigation problems in UWB-based positioning algorithms for dense environments[J]. Annals of Telecommunications - Annales Des Télécommunications, 2010,65(5): 301-311.
[10]	NESSA A , ADHIKARI B , HUSSAIN F ,et al. A survey of machine learning for indoor positioning[J]. IEEE Access, 2020(8): 214945-214965.
[11]	KRISTENSEN J B , MASSANET G M , JENSEN O K ,et al. Non-line-of-sight identification for UWB indoor positioning systems using support vector machines[C]// Proceedings of 2019 IEEE MTT-S International Wireless Symposium (IWS). Piscataway:IEEE Press, 2019: 1-3.
[12]	BARRAL V , ESCUDERO C J , GARCíA-NAYA J A , ,et al. NLOS identification and mitigation using low-cost UWB devices[J]. Sensors, 2019,19(16): 3464.
[13]	MARANò S , GIFFORD W M , WYMEERSCH H ,et al. NLOS identification and mitigation for localization based on UWB experimental data[J]. IEEE Journal on Selected Areas in Communications, 2010,28(7): 1026-1035.
[14]	LI W J , ZHANG T T , ZHANG Q Y . Experimental researches on an UWB NLOS identification method based on machine learning[C]// Proceedings of 2013 15th IEEE International Conference on Communication Technology. Piscataway:IEEE Press, 2014: 473-477.
[15]	RAMADAN M , SARK V , GUTIERREZ J ,et al. NLOS identification for indoor localization using random forest algorithm[C]// Proceedings of 22nd International ITG Workshop on Smart Antennas.[S.l.:s.n.], 2018: 1-5.
[16]	JIANG C H , SHEN J C , CHEN S ,et al. UWB NLOS/LOS classification using deep learning method[J]. IEEE Communications Letters, 2020,24(10): 2226-2230.
[17]	ZHU Y P , XIA W W , YAN F ,et al. NLOS identification via AdaBoost for wireless network localization[J]. IEEE Communications Letters, 2019,23(12): 2234-2237.
[18]	OLEJNICZAK A , BLASZKIEWICZ O , CWALINA K K ,et al. Deep learning approach for LOS and NLOS identification in the indoor environment[C]// Proceedings of 2020 Baltic URSI Symposium (URSI). Piscataway:IEEE Press, 2020: 104-107.
[19]	WU C , HOU H W , WANG W J ,et al. TDOA based indoor positioning with NLOS identification by machine learning[C]// Proceedings of 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP). Piscataway:IEEE Press, 2018: 1-6.
[20]	KILI? Y , ALI A J , MEIJERINK A ,et al. The effect of human-body shadowing on indoor UWB TOA-based ranging systems[C]// Proceedings of 2012 9th Workshop on Positioning,Navigation and Communication. Piscataway:IEEE Press, 2012: 126-130.
[21]	何杰, 吴雅南, 段世红 ,等. 人体对UWB测距误差影响模型[J]. 通信学报, 2017,38(S1): 58-66.
	HE J , WU Y N , DUAN S H ,et al. Model of human body influence on UWB ranging error[J]. Journal on Communications, 2017,38(S1): 58-66.
[22]	LI X F , HE J , XU L Y ,et al. The effect of multipath and NLOS on TOA ranging error and energy based on UWB[C]// Proceedings of 2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW). Piscataway:IEEE Press, 2016: 1-2.
[23]	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]// Proceedings of 2022 24th International Conference on Advanced Communication Technology (ICACT). Piscataway:IEEE Press, 2022: 141-144.
[24]	CWALINA K K , OLEJNICZAK A , BLASZKIEWICZ O ,et al. Fast fading influence on the deep learning-based LOS and NLOS identification in wireless body area networks[C]// Proceedings of 2021 34th General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS). Piscataway:IEEE Press, 2021: 1-4.
[25]	BHARADWAJ R , ALOMAINY A , KOUL S K . Experimental investigation of body-centric indoor localization using compact wearable antennas and machine learning algorithms[J]. IEEE Transactions on Antennas and Propagation, 2022,70(2): 1344-1354.
[26]	FERREIRA A G , FERNANDES D , BRANCO S ,et al. Feature selection for real-time NLOS identification and mitigation for bodymounted UWB transceivers[J]. IEEE Transactions on Instrumentation and Measurement, 2021(70): 1-10.
[27]	CWALINA K K , RAJCHOWSKI P , BLASZKIEWICZ O ,et al. Deep learning-based LOS and NLOS identification in wireless body area networks[J]. Sensors, 2019,19(19): 4229.
[28]	WEI J Y , WANG H , SU S ,et al. NLOS identification using parallel deep learning model and time-frequency information in UWB-based positioning system[J]. Measurement, 2022(195): 111191.
[29]	KOLAKOWSKI M , MODELSKI J . First path component power based NLOS mitigation in UWB positioning system[C]// Proceedings of 2017 25th Telecommunication Forum (TELFOR). Piscataway:IEEE Press, 2018: 1-4.
[30]	GURURAJ K , RAJENDRA A K , SONG Y ,et al. Real-time identification of NLOS range measurements for enhanced UWB localization[C]// Proceedings of 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN). Piscataway:IEEE Press, 2017: 1-7.
[31]	LinkTrack datasheet[EB]. 2021.
[32]	Decawave Ltd. DWI000 USER MANUAL[EB]. 2017.
[33]	ALBAIDHANI A , MORELL A , VICARIO J L . Ranging in UWB using commercial radio modules:experimental validation and NLOS mitigation[C]// Proceedings of 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN). Piscataway:IEEE Press, 2016: 1-7.
[34]	KIM D H , KWON G R , PYUN J Y ,et al. NLOS identification in UWB channel for indoor positioning[C]// Proceedings of 2018 15th IEEE Annual Consumer Communications ＆ Networking Conference (CCNC). Piscataway:IEEE Press, 2018: 1-4.
[35]	王奕森, 夏树涛 . 集成学习之随机森林算法综述[J]. 信息通信技术, 2018,12(1): 49-55.
	WANG Y S , XIA S T . A survey of random forests algorithms[J]. Information and Communications Technologies, 2018,12(1): 49-55.
[36]	方匡南, 吴见彬, 朱建平 ,等. 随机森林方法研究综述[J]. 统计与信息论坛, 2011,26(3): 32-38.
	FANG K N , WU J B , ZHU J P ,et al. A review of technologies on random forests[J]. Statistics ＆ Information Forum, 2011,26(3): 32-38.

Metrics

Recommended 0

No Suggested Reading articles found!

数据集	采集设备	距离/m	数据集大小/组
S1	LinkTrack S	1.2～7.8	72 000
S2	LinkTrack S	1.2～8.4	130 000
S3	ULM1	1.2～8.4	104 000

数据集	naive Bayes	SVM	KNN	FNN	RF
S1	0.56	48.47	0.55	17.86	5.46
S2	1.22	119.29	1.00	79.76	12.71
S3	0.29	104.86	0.52	32.24	12.81

遮挡物	准确率	召回率
门	96.04%	97.24%
墙壁	99.97%	99.94%
家具	100%	100%

A UWB NLOS identification method under pedestrian occlusion

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 13

References 36

Related Articles 4

Metrics

Recommended 0

数据集	文献[18]方法		文献[19]方法		本文方法
数据集	准确率	召回率	准确率	召回率	准确率	召回率
S1	79.53%	79.98%	54.82%	50.85%	99.05%	98.54%
S2	77.04%	78.32%	54.77%	55.60%	99.32%	98.94%
S3	86.44%	79.10%	53.01%	81.14%	98.81%	98.54%

[1]	Yu YAO, Yanjie LI, Lenan WU, Pu MIAO, Xiaoyu TANG. Cognitive waveform design for radar-communication transceiver networks [J]. Chinese Journal on Internet of Things, 2021, 5(4): 71-80.
[2]	Fuzhan WANG, Xiaorong ZHU, Meijuan CHEN, Hongbo ZHU. High-precision indoor wireless positioning method based on generative adversarial network [J]. Chinese Journal on Internet of Things, 2021, 5(2): 107-115.
[3]	Shaowei SHAN,Weidong YANG,Le XIAO,Ke WANG. Wi-Pest:a method for detecting stored grain pests based on CSI [J]. Chinese Journal on Internet of Things, 2020, 4(4): 51-61.
[4]	HANGE C,Weijie LENG. Summary of wireless indoor positioning technology in industry Internet [J]. Chinese Journal on Internet of Things, 2020, 4(2): 129-135.