A nonlinear smoother for target tracking in asynchronous wireless sensor networks

Generally, multiple sensors are deployed to track a target synchronously in wireless sensor networks. However, asynchronous measurements exist intrinsically in multi-rate multi-sensor systems. Asynchronous measurements may also emerge in acoustic sensor networks, owing to the low propagation speed of acoustic signals. In order to handle the target tracking problem with asynchronous measurements, a nonlinear smoothing algorithm based on the fixed-point smoother and the square-root cubature Kalman filter is derived and applied in asynchronous wireless sensor networks for the first time. The estimation precision of the states increases along with the smoothing process, and a sensor can always obtain the optimal estimate of a state before its own next measurement by using the proposed algorithm. The numerical simulations demonstrate that, thanks to the smoothing effect of the fixed-point smoother, the proposed algorithm can obtain not only the remarkable position estimation results of the target, but also even better velocity estimation results. In addition, the proposed algorithm can obtain much more states' estimates than benchmark synchronous target-tracking algorithms, under the same condition of measurement count and communication cost.