A PLLS-PKF Method for Target Tracking of DOA Measurement Sensor Networks.

In this paper, we propose a novel tracking algorithm that adopts both Pseudo-linear least square method and Pseudo-linear Kalman Filtering (PLLS-PKF) for target tracking using bearing only sensor networks. The conventional Pseudo-linear Kalman Filtering (PKF) is one of the practice tracking methods in this situation. Limited by the data accuracy, the outputs of PKF tend to be unstable by incorporating signal data with large error. Using PLLS localization to yield one step iteration updating process, the modified method can help to improve the estimation accuracy. Both numerical simulations and real experiment are conducted to illustrate that the PLLS-PKF method can provide better tracking performance compared with the conventional PKF method.