Maneuvering Target Tracking Using the Optimal Stochastic Jump Filtering Algorithm

Abstract

The problem of maneuvering target tracking is addressed in this paper. Based on the 'current' statistical model, two-structured stochastic jump system is constructed in which the acceleration covariance switching to each other. The parameters are estimated with optimal stochastic jump filtering algorithm, and the tracking ability is improved much more after using the covariance non-linear adjustment. Finally, numerical simulation is performed and the simulation result demonstrates that this tracking algorithm outperforms original adaptive algorithm and improves the precision and tracking ability at the same time.