Ground Clutter Canceling with a Regression Filter

Abstract

This paper explores ground clutter filtering with a class of cancelers that use regression. Regression filters perform this task in a simple manner, resulting in similar or better performance than the fifth-order elliptic filter implemented in the WSR-88D. Assuming a slowly varying clutter signal, a suitable projection of the composite signal is used to notch a band of frequencies at either side of zero Doppler frequency. The complexity of this procedure is reduced by using a set of orthogonal polynomials. The frequency response of the resulting filter is related to the number of samples in each input block and the maximum order of approximating polynomials. Through simulations, it is demonstrated that the suppression characteristic of this filter is better than that of step-initialized infinite impulse response filters, whereby transients degrade the theoretical frequency response. The performance of regression filters is tested with an actual weather signal, and their efficiency in ground clutter canceling is demonstrated.