Converted Measurement Sigma Point Kalman Filter for Bistatic Sonar and Radar Tracking

Abstract

Tracking with bistatic sonar or radar measurements is challenging due to the fact that the measurements are nonlinear functions of the Cartesian state. The performance of existing approaches, including the extended Kalman filter and sigma point Kalman filters, such as the unscented Kalman filter and cubature Kalman filter, may not be acceptable in terms of mean square error or tracker consistency (i.e., the tracker's state estimation error covariance is not statistically consistent with the actual estimation errors). This paper generalizes the sigma point Kalman filter (SPKF) as a converted measurement SPKF. The resulting converted measurement sigma point Kalman filter is demonstrated to have improved performance over the conventional SPKF when employed in a bistatic tracking scenario.