Research on an Adaptive Maneuvering Target Tracking Algorithm

Abstract

The maneuverability of modern targets becomes more and more complex and variable, which raises higher requirements on the tracking performance of detection systems. Especially the stable and accurate tracking of maneuvering targets is more critical. For the problem that statistical properties of detection system noise are unknown and the state of motion of targets is complex and variable, a new adaptive maneuvering target tracking algorithm is proposed. The algorithm adopts the combination of adaptive Kalman filtering under the spherical coordinate system and its counterpart under the Cartesian coordinate system. The adaptive Kalman filtering algorithm under the spherical coordinate system is based on Sage-Husa noise statistics estimator to estimate the statistical property of measurement noise. In the Cartesian coordinate system, the Singer model is used to describe the target motion. Relevant results of the adaptive Kalman filtering algorithm under the spherical coordinate system are used to achieve high-precision estimation of target motion information. Simulation results show that the proposed algorithm has satisfactory tracking accuracy.