Adaptive Localization Algorithm Based on Distributed Compressed Sensing in Wireless Sensor Networks
After wireless sensor networks are divided into multiple grids, the number of Localization targets is sparse compared with the grid number, accordingly the localization problem is turned into the sparse signal rebuild problem. In view of spatial sparsity of targets distribution, Distributed Compressed Sensing (DCS) method is adopted to conduct precise localization. An Adaptive DCS-based Localization (ADCSL) algorithm is proposed. Within the algorithm, it is estimated that the original location of the target node is based on the joint possibility density function, which positions the best of the neighbor nodes and gives them some trust through cooperative procedure method. Moreover, a measured matrix is established on the basis of the energy declining characteristics of the target, and multiple categories of targets localization algorithm is also designed. Eventually, compared with other target localization methods, experimental results and simulation analysis indicate that the ADCSL algorithm leads to a better precision from the perspective of the average localization error