Multiple Targets Sparse Information Localization In Wireless Sensor Networks

For the multiple targets localization in Wireless Sensor Networks(WSNs), limited by environmental factors and information extraction technology, the physical information for localization presents strong incompleteness. Multiple target localization under the incomplete information is the emphasis and difficulty of the present WSNs technology research. Using compressed sensing theory, the thesis proposes a WSNs range-free multiple targets sparse information localization algorithm(CS-STI).The method gets the measurements through determining whether there are targets and the number of targets within the node's sensing radius, and it doesn't depend on any extra hardware measurements in the process. After the sensor nodes obtain information collections for targets, targets can be reconstructed using recovery algorithm of the CS. The main content of this paper is as follows:(1) Build a network model, the WSNs monitoring region was divided into a plurality of small grids. Sensors and targets are randomly dropped in the grids. The targets position is defined as a sparse vector in the discrete space.(2) Define the parameters matrix, the number of detected targets by sensor nodes is expressed as the product of measure matrix, sparse matrix and sparse vector in compressive sensing theory. And the physical meaning and expression of the matrices are given.(3) The measurements matrix is orthogonalized to meet the requirements of the reconstruction algorithm. Basis Pursuit(BP) and Orthogonal Matching Pursuit(OMP) are applied to recover targets localization.(4) The algorithm simulation analysis, the range-free multiple targets sparse information localization algorithm based on the CS theory is simulated and compared with the traditional algorithm. The method performance varing with sensing radius, targets' number and sensors' number is analyzed.