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 to other existing filters while requiring a

relatively small computational load.