An approach based on baseband signals was proposed aiming at the range spread problem of multi-human target tracking in ultra wide band (UWB) radar. The RF echo was down-converted and decimated, and clutters were filtered out by moving target indication. The baseband CLEAN detection was applied to extract measurements. Then the initial state of the target was determined by clotting and jumping-window method. Joint probabilistic data association and Kalman filter were adopted for tracking. The experiment was conducted in three indoor environments. Results showed that the root mean square error (RMSE) of multi-target tracking was less than 0.26 m, while the data storage was reduced by 87.5% and the processing time for target detection was reduced by 39.7% compared with directly using RF echo.
Jin-hai ZHOU,Shi-yi ZHOU,Yang CHANG,Geng-jun WU,Yi-chuan WANG. Multi-human target tracking based on baseband signals of ultra wide band radar. Journal of ZheJiang University (Engineering Science), 2021, 55(6): 1208-1214.
Tab.1Comparison of target detection processing time
Fig.8Results of CLEAN detection (S1)
雷达帧
该帧所有量测
量测凝聚结果
1
33
33
2
103
103
3
36,78,84
36,81
4
31,37,79,83
34,81
5
80
80
6
35,37,52,78
36,52,78
7
31,33,43,51,76
32,43,51,76
8
76,99
76,99
Tab.2Measurement clotting
Fig.9Tracking results of measured process S1~S4
实测环境
过程
目标
RMSE/m
实验室
S1
目标1
0.17
实验室
S1
目标2
0.26
过道
S2
目标1
0.15
过道
S2
目标2
0.11
休息厅
S3
目标1
0.17
休息厅
S3
目标2
0.13
实验室
S4
目标1
0.20
实验室
S4
目标2
0.17
Tab.3RMSE of tracking
[1]
FLORENCE D, GARBAY C, RIALLE V Learning recurrent behaviors from heterogeneous multivariate time-series[J]. Artificial Intelligence in Medicine, 2007, 39 (1): 25- 47
doi: 10.1016/j.artmed.2006.07.004
[2]
FUCHSBERGER V. Ambient assisted living: elderly people’s needs and how to face them[C]// Proceedings of 1st ACM International Workshop on Semantic Ambient Media Experiences. Vancouver: ACM, 2008: 27-31.
[3]
文顺菊. 我国失能老人的照护需求与照护成本测算[D]. 成都: 西南财经大学, 2016. WEN Shun-ju. Calculation of care needs and care costs of disabled elderly in China[D]. Chengdu: Southwestern University of Finance and Economics, 2016.
[4]
WILD K, BOISE L, LUNDELL J, et al Unobtrusive in-home monitoring of cognitive and physical health: reactions and perceptions of older adults[J]. Journal of Applied Gerontology, 2008, 27 (2): 181- 200
doi: 10.1177/0733464807311435
[5]
BARREET T W History of ultra wideband communications and radar[J]. Microwave Journal, 2001, 44 (2): 22- 46
[6]
费元春. 超宽带雷达理论与技术[M]. 北京: 国防工业出版社, 2010.
[7]
周金海, 王依川, 佟鲸鲆, 等 基于慢时间分割的超宽带雷达步态识别[J]. 浙江大学学报: 工学版, 2020, 54 (2): 283- 290 ZHOU Jin-hai, WANG Yi-chuan, TONG Jing-ping, et al Ultra wide band radar gait recognition based on slow-time segmentation[J]. Journal of Zhejiang University: Engineering Science, 2020, 54 (2): 283- 290
[8]
JOKANOVIC B, AMIN M, AHMAD F. Radar fall motion detection using deep learning[C]// 2016 IEEE Radar Conference. Philadelphia: IEEE, 2016: 1-6.
[9]
WANG Yi-chuan, ZHOU Jin-hai, TONG Jing-ping, et al UWB-radar-based synchronous motion recognition using time-varying range-Doppler images[J]. IET Radar, Sonar and Navigation, 2019, 13 (12): 2131- 2139
doi: 10.1049/iet-rsn.2019.0240
[10]
RAHEEL M S, COYTE J, TUBBAL F, et al. Breathing and heartrate monitoring system using IR-UWB radar[C]// 2019 13th International Conference on Signal Processing and Communication Systems. Gold Coast: IEEE, 2019: 1-5.
[11]
KIM S H, GEEM Z W, HAN G T A novel human respiration pattern recognition using signals of ultra-wideband radar sensor[J]. Sensors, 2019, 19 (15): 3340
doi: 10.3390/s19153340
[12]
CHOI J W, NAM S S, CHO S H Multi-human detection algorithm based on an impulse radio ultra-wideband radar system[J]. IEEE Access, 2016, 4: 10300- 10309
doi: 10.1109/ACCESS.2016.2647226
[13]
武江涛. 雷达扩展目标跟踪算法研究[D]. 西安: 西安电子科技大学, 2014. WU Jiang-tao. Research of radar extended target tracking[D]. Xi’an: Xidian University, 2014.
[14]
SAKAMOTO T, SATO T, AUBRY P J, et al Texture-based automatic separation of echoes from distributed moving targets in UWB radar signals[J]. IEEE Transactions of Geoscience and Remote Sensing, 2015, 53 (1): 352- 361
doi: 10.1109/TGRS.2014.2322438
[15]
李剑菡. 基于卷积神经网络的人体生命体征和多目标检测算法研究[D]. 北京: 北京邮电大学, 2019. LI Jian-han. Human vital signs and multi-targets detection based on convolutional neural network[D]. Beijing: Beijing University of Post and Telecommunications, 2019.
[16]
刘金超. 超宽带雷达人体目标检测与跟踪[D]. 长沙: 国防科学技术大学, 2014. LIU Jin-chao. Human target detection and tracking with ultra-wideband radar[D]. Changsha: National University of Defense Technology, 2014.
[17]
CHANG S H, SHARAN R, WOLF M, et al People tracking with UWB radar using a multiple-hypothesis tracking of clusters (MHTC) method[J]. International Journal of Social Robotics, 2010, 2 (1): 3- 18
doi: 10.1007/s12369-009-0039-x
[18]
RICHARDS M A. 雷达信号处理基础[M]. 刑孟道, 王彤, 李真芳, 等, 译. 2版. 北京: 电子工业出版社, 2017.
[19]
NGUYEN V, PYUN J Location detection and tracking of moving targets by a 2D IR-UWB radar system[J]. Sensors, 2015, 15 (3): 6740- 6762
doi: 10.3390/s150306740
[20]
HALL D L. 多传感器数据融合手册[M]. 杨露清, 耿伯英, 译. 北京: 电子工业出版社, 2008.
[21]
ANDERSON N, MICHAELSEN J A, BAGGA S, et al A 118-mW pulse-based radar SoC in 55-nm CMOS for non-contact human vital signals detection[J]. IEEE Journal of Solid-State Circuits, 2017, 52 (12): 3421- 3433
doi: 10.1109/JSSC.2017.2764051
[22]
CRAMER R J, SCHOLTZ R A, WIN M Z Evaluation of an ultra-wide-band propagation channel[J]. IEEE Transactions on Antennas and Propagation, 2002, 5 (50): 561- 570
[23]
TSAO J, PORRAT D Prediction and modeling for the time-evolving ultra-wideband channel[J]. IEEE Journal of Selected Topics in Signal Processing, 2007, 1 (3): 340- 356
doi: 10.1109/JSTSP.2007.906662
[24]
RICHARD L. Quadrature signals: complex, but not complicated [EB/OL]. [2020-01-04]. https://www.ieee.li/pdf/essay/quadrature_signals.pdf.
[25]
XeThru explorer [EB/OL]. [2020-01-04]. https://www.xethru.com/community/resources/categories/xethru-explorer.3/.