IMMKF与Chan-Taylor算法的协同定位

doi:10.16180/j.cnki.issn1007-7820.2023.12.001

Abstract

Abstract:

TDOA(Time Difference of Arrival) rangmg method is a typical opproach for UWB(Ultra Wide Band) indoor location.A Chan-Taylor-IMMKF(Interacting Multiple Model Kalman Filter) localization technique is suggested in this study to address the unavoidable random error and inaccurate location of targets with changing motion states. With the addition of the adaptive algorithm IMM, the algorithm is made up of the Chan-Taylor weighting algorithm and the Kalman filter algorithm. The Chan-Taylor weighting procedure is used to acquire the target estimated coordinates for the first time. The coordinate value is then used as the measurement value for the Kalman filter of the adaptive algorithm IMM, and the target coordinates are filtered many times. The target's final estimated coordinates are provided by the final weighting. The experimental results reveal that the filtered Chan-Taylor weighting algorithm outperforms both conventional Kalman filtering and the unfiltered Chan-Taylor weighting algorithm. The algorithm successfully lowers the system's random error and fixes the issue that the conventional Kalman filter cannot track the significant error when the target abruptly changes its motion state,and the mean error standard deviation is controlled within 15 cm.

Key words: indoor location algorithm, ultra wide-band, TDOA, Chan algorithm, Taylor algorithm, RWGH, Kalman filtering, interacting multiple model

CLC Number:

TN929

WANG Xinyue,YU Huimin,HU Luning. Cooperative Localization of IMMKF and Chan-Taylor Algorithm[J].Electronic Science and Technology, 2023, 36(12): 1-8.

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References 18

[1]	Sahu P K, Wu E H-K, Sahoo J. DuRT:Dual RSSI trend based localization for wireless sensor networks[J]. IEEE Sensors Journal, 2013, 13(8):3115-3123. doi: 10.1109/JSEN.2013.2257731
[2]	聂睿. 一种基于UWB定位的误差抑制TDOA算法研究[D]. 海口: 海南大学, 2020:14-15.
	Nie Rui. Research on an error suppression TDOA al-gorithm based on UWB positioning[D]. Haikou: Hainan University, 2020:14-15.
[3]	葛丽丽. 基于UWB的高精度室内定位及时钟同步算法的研究[D]. 北京: 北京邮电大学, 2019:42-45.
	Ge Lili. Research on high precision indoor location and cloock synchronization algorithms based on UWB[D]. Beijing: Beijing University of Posts and Telecommunications, 2019:42-45.
[4]	Qing Z L, Zhen D Y, Yan L Z, et al. UWB LOS/NLOS identification in multiple indoor environments using deep learning methods[J]. Physical Communication, 2022(52):101695-101706.
[5]	尹蕾, 李瑶, 刘洛琨, 等. 一种基于卡尔曼滤波的UWB定位算法[J]. 通信技术, 2008(2):10-12.
	Yin Lei, Li Yao, Liu Luokun, et al. Aultra wideband location method based on Kalman filter[J]. Communications Technology, 2008(2):10-12.
[6]	苏翔. 基于扩展卡尔曼滤波器的混合TDOA/AOA室内定位技术的研究[J]. 数字技术与应用, 2013(8):56-57,59.
	Su Xiang. Research on extended Kalman filter hybrid TDOA/AOA indoor location technology[J]. Digital Technology & Application, 2013(8):56-57,59.
[7]	Li X, Yang S. The indoor real-time 3D localization algorithm using UWB[C]. Beijing: Proceedings of the International Conference on Advanced Mechatronic Systems, 2015:337-342.
[8]	仲江涛, 秦斌, 吴健春, 等. 基于Kalman滤波的Chan室内定位算法改进[J]. 通信技术, 2017, 50(10):2223-2228.
	Zhong Jiangtao, Qin Bin, Wu Jianchun, et al. Modified Chan in-door positioning algorithm based on Kalman filter[J]. Communications Technology, 2017, 50(10):2223-2228.
[9]	Radosavljević, Zvonko. Analysis of IMM algorithm for maneuvering target tracking[J]. Vojnote-hnički Glasnik, 2005,53(3-4):280-286.
[10]	史策. 基于时间反演的超宽带通信系统抗干扰研究[D]. 重庆: 重庆邮电大学, 2020:7-14.
	Shi Ce. Research on antiinterference of UWB communication system based on time reversal[D]. Chongqing: Chongqing University of Posts and Telecommunications, 2020:7-14.
[11]	范一惟. 基于超宽带室内定位的时间同步算法研究[D]. 桂林: 桂林电子科技大学, 2021:18-20.
	Fan Yiwei. Research on time synchronization algorithm based on UWB indoor positioning[D]. Guilin:Guilin University of Electronic Technology, 2020:18-20.
[12]	黄泽华. 基于UWB的室内定位系统研究[D]. 济南: 山东大学, 2021:14-18.
	Huang Zehua. Research on indoor positioning system based on UWB[D]. Jinan: Shandong University, 2021:14-18.
[13]	凌威. 基于UWB技术的区域定位系统设计与实现[D]. 哈尔滨: 哈尔滨工业大学, 2021:20-22.
	Ling Wei. Design and impiementation of area position system based on UWB technology[D]. Harbin:Harbin Institute of Technology, 2021:20-22.
[14]	白晓娟, 道伟, 关露, 等. 室内复杂环境处理及泰勒公式改进定位算法研究[J]. 电子测量技术, 2021, 44(3):55-59.
	Bai Xiaojuan, Dao Wei, Guan Lu, et al. Research on indoor complex environment processing and improved positioning algorithm of Taylor formula[J]. Electronic Measurement Technology, 2021, 44(3):55-59.
[15]	Yuan Y, Li Y, Liu Z, et al. A three dimensional tracking scheme for underwater non-cooperative objects in mixed LOS and NLOS environment[J]. Peer-to-Peer Networking and Applications, 2019, 12(5):1369-1384. doi: 10.1007/s12083-018-0678-5
[16]	Chen Y, Ze D A, Chen H, et al. A UWB location algorithm based on adaptive Kalman filter[J]. Journal of Physics: Conference Series, 2021, 57(1):01276-01283.
[17]	胡青松, 李世银. 无线定位技术[M]. 北京: 科学出版社, 2020:117-122.
	Hu Qinsong, Li Shiyin. Wireless localization technologies[M]. Beijing: Science Press, 2020:117-122.
[18]	黄小平, 王岩. 卡尔曼滤波原理及应用 MATLAB仿真[M]. 北京: 电子工业出版社, 2015:119-120.
	Huang Xiaoping, Wang Yan. Kalman filtering principle and application MATLAB simulation[M]. Beijing: Publishing House of Electronics Industry, 2015:119-120.

算法	最大误差	最小误差	平均误差
Chan-Taylor	96.335 00	1.081	28.448
Chan-Taylor-IMMKF	62.547 81	0.475	13.916

Cooperative Localization of IMMKF and Chan-Taylor Algorithm

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