Application of Mixed Kalman Filter to Passive Radar Target Tracking

Abstract

To improve the estimation accuracy of the error covariance matrix in Unscented Kalman Filter (UKF). With the passive radar target tracking model, a novel Mixed Kalman Filter (MKF) is proposed, Firstly, the UKF is used to conduct a posteriori estimate for target state, and then re-establish a measurement equation, the posteriori estimated value of state by UKF is transformed into a measured value of the new measurement equation, and through linear Kalman Filter the state is best estimated secondly, improving the precision of target state estimation. Experimental results indicate that MKF algorithm significantly improves the performance of passive radar target tracking, compared with the Extended Kalman Filter (EKF) and UKF.