Design of nearly constant velocity filters for radar tracking of maneuvering targets

Abstract

When tracking maneuvering targets with conventional algorithms, the process noise standard deviation used in the nearly constant velocity Kalman filter is selected vaguely in relation to the maximum acceleration of the target. In recent years, the deterministic tracking index was introduced and used to develop a relationship between the maximum acceleration and the process noise variance that minimizes the maximum mean squared error (MMSE) in position. A lower bound on the process noise variance was also developed. The process noise variance was expressed in terms of the maximum acceleration, duration of the maneuver in number of measurement periods, and deterministic tracking index. In this paper, the design methods for nearly constant velocity filters are extended from Cartesian measurements to polar or spherical measurements found in radar systems. The effectiveness of the design methods for radar tracking are confirmed via Monte Carlo simulations.