Abstract
In this paper, we propose a reduced-complexity decentralized positioning (RCDP) algorithm for target positioning and tracking in wireless sensor networks. The proposed approach formulates the target location estimation as a nonlinear least squares (NLS) problem using the received signal strength (RSS) information of the local sensor nodes, and then applies the RCDP algorithm iteratively to estimate the target location. In the localization process, a participating sensor node updates the target's estimated location and passes the updated estimate on to the next participating sensor node, which calculates a new estimated target location. We also provide a convergence analysis of the RCDP-based method to guarantee that the proposed iterative localization process converges. Computer simulation results show that our proposed method has a higher estimation accuracy and a better tracking efficiency than previous related methods in both stationary and moving target scenarios.