An efficient filtering algorithm for improved radar tracking

The extended Kalman filter (EKF) has been widely used as a nonlinear filtering method for radar tracking problems. However, it has been found that in case cross-range measurement errors of the target position are large, the performance of the conventional EKF degrades considerably due to non-negligible nonlinear effects. In this paper, a new filtering algorithm for improving the radar tracking performance is developed based on the fact that the correct evaluation of the measurement error covariance can be made possible by doing it with respect to the Cartesian state vector. The resulting filter may be viewed as a modification of the EKF in which the variance of the range measurement errors is re-evaluated at each time step and the measurements are sequentially processed in the order of azimuth and range. Computer simulation results show that the proposed method achieves superior performance than other existing filters while requiring a relatively small computational load.