Decentralized Positioning and Tracking Based on a Weighted Incremental Subgradient Algorithm for Wireless Sensor Networks

In this paper, we propose a weighted incremental subgradient (WIG) algorithm for positioning and tracking in wireless sensor networks. We formulate the location estimation of a target as a weighted least squares (WLS) problem by taking weights of the local estimates based on the reliability information of distance estimation, and then solve the WLS problem in an iterative, decentralized manner using the WIG algorithm, where a message-passing algorithm is used for inter-sensor communication and for adaptively selecting the participating sensors as the target moves around the area. During each iteration, a participating sensor estimates a current target's location, which is passed to the next participating sensor for making new estimation. The update process continues among the participating sensors in the close vicinity of the target. In addition, a convergence analysis of the WIG algorithm is given to show that the proposed iterative positioning process converges. Computer simulation results demonstrate that, as compared with previous methods, our proposed scheme has a faster convergence rate and higher estimation accuracy in both stationary and moving target scenarios.