A parametric approach to hot clutter cancellation

Many reduced dimension STAP algorithms have been developed for airborne radar applications which rely on a stationary Doppler component of the interference in order to maintain acceptableperformance. Two cases where this assumption is violated are when the gmund clutter contains intrinsic clutter motion (ICM) and when hot clutter is present. In addition to the non-stationa y Doppler component, hot clutter contains non-zem correlations in fast-time (across range bins) as well. This paper will present an algorithm designed to mitigate both gmund clutter and hot clutter in the same step using a two dimensional vector airtoregressive model to whiten the data in.space, fast-time, and slow-time. This is an extension of the Space-lime AutoRegwssive (STAR) filter that we have previously proposed. Using a simulated data set for circular array STAP augmented with synthetic hot clutter; we demonstrate that the extensions we present do result in a signiJ5cant performance increase over the standard STARJilter: In addition we also show that the STARjilters have a narmwer clutter notch than the optimized pre-Dopplerfilter when a finite sample support is used to train thefilters.