A Bayesian Approach for Targets Localization Using Binary Sensor Networks

This paper presents a Bayesian approach to energy efflcient and data-efflcient 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 flnd 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.