An Asynchronous Data Fusion Algorithm for Target Detection Based on Multi-Sensor Networks

被引：18

作者：

Zhang, Ke ^{[1
]}

Wang, Zeyang ^{[2
]}

Guo, Lele ^{[3
]}

Peng, Yuanyuan ^{[1
]}

Zheng, Zhi ^{[3
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China

[2] New H3C Technol Co Ltd, Chengdu 610000, Peoples R China

[3] Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu 611731, Peoples R China

来源：

IEEE ACCESS | 2020年 / 8卷

关键词：

Asynchronous fusion; multi sensors; track fusion; track quality with multiple model; TRACK FUSION; BIAS;

D O I：

10.1109/ACCESS.2020.2982682

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The time interval of the observational data changes irregularly because of the difference of sensors' sampling rate, the communication delay and the target leaving observation region of the sensor sometimes. These problems of asynchronous observation data greatly reduce the tracking accuracy of the multi-sensors system. Therefore, asynchronous data fusion system is more practical than synchronous data fusion system, and worthier of study. By establishing an asynchronous track fusion model with irregular time interval of observation data and combining with the Track Quality with Multiple Model (TQMM), an asynchronous track fusion algorithm with information feedback is proposed, and the TQMM is used for weight allocation to improve the performance of the asynchronous multi-sensor fusion system. The simulation result shows that the algorithm has better tracking performance compared with other algorithms, so that this kind of problem of track-to-track fusion for asynchronous sensors is solved effectively.

引用

页码：59511 / 59523

页数：13

共 28 条

[1] Track-to-Track Fusion With Asynchronous Sensors Using Information Matrix Fusion for Surround Environment Perception
Aeberhard, Michael
Schlichthaerle, Stefan
Kaempchen, Nico
Bertram, Torsten
[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2012, 13 (04) : 1717 - 1726
[2] Aeberhard M, 2012, 2012 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV), P1, DOI 10.1109/IVS.2012.6232115
[3] Bardwaj AA, 2008, ICSCN 2008: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING COMMUNICATIONS AND NETWORKING, P250
[4] Simultaneous spatiotemporal bias and state estimation for asynchronous multi-sensor system
Bu, Shizhe
Zhou, Chang
Zhou, Gongjian
[J]. JOURNAL OF ENGINEERING-JOE, 2019, 2019 (19): : 5824 - 5828
[5] Asynchronous Multisonar Data Integration Approach Used to Detect Sparse Targets
Chen, Yang
Sheng, Xueli
Guo, Longxiang
Li, Pengfei
Hao, Haoyan
Yin, Jingwei
[J]. IEEE ACCESS, 2019, 7 : 37908 - 37917
[6] Hu YY, 2013, CHINESE J ELECTRON, V22, P525
[7] Hui-dong G., 2006, P IEEE 9 INT C INF F, P1
[8] Jeffrey T. W., 1989, Proceedings of the 1989 IEEE National Radar Conference (Cat. No.89CH2685-6), P76, DOI 10.1109/NRC.1989.47619
[9] Ke Zhang, 2019, Simulation Tools and Techniques. 11th International Conference, SIMUtools 2019. Proceedings. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST 295), P205, DOI 10.1007/978-3-030-32216-8_20
[10] Radar/ESM anti-bias track association algorithm based on track distance vector detection
Li, Baozhu
Dong, Yunlong
Huang, Gaodong
Chen, Xiaolong
Guan, Jian
[J]. JOURNAL OF ENGINEERING-JOE, 2019, 2019 (21): : 7563 - 7567

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