An improved algorithm for target trajectory prediction in the active protection system based on converted measurement Kalman filter

Abstract

Fast and accurate prediction of the trajectory is the prerequisite for the successful interception of the incoming target by the active protection system (APS) of armoured vehicles. The traditional least-squares algorithm is susceptible to large errors in the initial stage of the tracking process, and the prediction of interception points converges slowly. This paper proposes an interception point prediction algorithm that combines the Kalman filter and least-squares estimation. By dynamically determining the convergence of the least-squares prediction, a converted measurement Kalman filter is used in the initial tracking stage to obtain higher prediction accuracy of the interception point. Simulations are performed in the scenario of the interception process of the APS. The results prove that the algorithm can effectively accelerate the convergence of the interception point prediction process.