Description: 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.
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