A Bayesian Approach for Targets Localization Using Binary Sensor Networks

Lan, Gongjin1; Hu, XiaoLi2; Hao, Qi1

10.1109/ISCID.2015.12

2015

2165-1701

2015 8TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID), VOL 2

2

453-456

Hangzhou, Zhejiang, China

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

This paper presents a Bayesian approach to energy-efficient and data-efficient target localization using binary sensor networks. The novelty of this work lies in that the methods of channel coding (code design, encoding and decoding) are used to solve the target localization problem. First, the binary sensor networks are constructed via Low-density parity-check (LDPC) matrices. As a result, the observation space targets is partitioned into many units that encoded into a set of binary codes. Then, when targets move around, the system measurements will be the encoded with those binary codes through OR operations. In the decoding stage, Bayesian inference algorithms are developed to find the unit of source target, which is complicated by OR logic operations. Compared with the match pursuit algorithm, the Bayesian approach is more robust against noise. Numerical simulations have demonstrated the advantages of the proposed approach.

Targets localization Binary sensor network Bayesian inference LDPC matrix Compressive sensing

第一 ; 通讯

英语

CPCI-S ; EI

Computer Science ; Engineering

Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic

WOS:000398913600105

20162902605065

Artificial intelligence ; Binary codes ; Computation theory ; Decoding ; Inference engines ; Satellite communication systems ; Sensor networks

Communication Satellites:655.2.1 ; Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory:721.1 ; Computer Software, Data Handling and Applications:723 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4

Web of Science

1.South Univ Sci & Technol China, Dept Elect & Elect Engn, Shenzhen, Peoples R China
2.Sun Yat Sen Univ, Sch Informat Sci & Technol, Guangzhou, Guangdong, Peoples R China

Lan, Gongjin,Hu, XiaoLi,Hao, Qi. A Bayesian Approach for Targets Localization Using Binary Sensor Networks[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2015:453-456.