An Improved Gaussian Particle Filtering Algorithm for Fixed Single Station Passive Location and Tracking
Liangqun Li, Sheng Hu-Sheng · 2020
In this paper, we propose an improved Gaussian particle filtering algorithm to improve the convergence performance and the location accuracy of single-station passive location tracking system. Moreover, in the proposed algorithm, the result of the unscented Kalman filtering is used as importance density function to avoid the resampling process in the particle filtering. Finally, Simulation experiments show that the proposed algorithm can be applied to a fixed single-station passive location system, can effectively improve the filtering accuracy, and the tracking performance is better than the Gaussian particle filter.