Sequential Asynchronous Filters for Target Tracking in Wireless Sensor Networks

Abstract

Asynchronous data fusion is inevitable for target tracking in asynchronous wireless sensor networks, where multiple sensors are required to locate a target collaboratively. The predicted estimates of the follow-up states based on the to-be-estimated state are first introduced to overcome the drawback that asynchronous measurements cannot be fused directly. Then, the sequential asynchronous Bayesian state estimation is deduced based on the predicted estimates. The proposed estimation process is comprised of two steps: 1) the prediction step and 2) the update step. Finally, sequential asynchronous filters based Kalman filter and particle filter are proposed. Simulations demonstrate that the proposed algorithms perform not only better than the benchmark algorithms with asynchronous measurements, but also better than the benchmark algorithm with synchronous measurements.