A Range-Free Multiple Target Localization Algorithm Using Compressive Sensing Theory in Wireless Sensor Networks

Considering the incompleteness of localization information in wireless sensor networks, the sensor network monitoring region was divided into a plurality of small grids. Sensors and targets are randomly dropped in the grids. Defining the targets position information as a sparse vector, a range-free multiple target localization algorithm using compressive sensing theory (MTLCS) was proposed. Only targets number sensed by sensor nodes is needed in the algorithm. It doesn't depend on extra hardware measurements. MTLCS can provide the targets position with sparse detected information. The number of targets detected by sensor nodes was expressed as the product of measurement matrix, sparse matrix and sparse vector in compressive sensing theory. Targets are localized with the sparse signal reconstruction. In order to explore MTLCS performance, BP and OMP are applied to recover targets localization. In case of grid number N=20 × 20, simulation is done with different measurement noise, sensing radius, targets number and sensor numbers. In case of 0.6≤r/n≤0.8, simulation results show that MTLCS has the localization error smaller than 30% without physical distance measurement. MTLCS can satisfy the requirements of target localization in wireless sensor network in the case of incomplete information.