Adaptive Radar Detection Algorithm Based on an Autoregressive GARCH-2D Clutter Model

Abstract

We propose a model for radar clutter that combinesan autoregressive (AR) process with a two-dimensional generalizedautoregressive conditional heteroscedastic (GARCH-2D) process.Based on this model, we derive an adaptive detection test, calledAR-GARCH-2D detector, for a target with knownDoppler fre-quency and unknown complex amplitude. Using real radar data,we evaluate its performance for different model orders, and we usea model selection criteria to choose the bestfit to the data. The re-sulting detector is not the constant false alarm rate (CFAR) withrespect to the process coefficients, but we show that in practical sit-uationsitisveryrobust.Finally,wecompare the AR-GARCH-2Ddetector performance with the performance of the generalized like-lihood ratio test (GLRT), the adaptive linear-quadratic (ALQ), andthe autoregressive generalized likelihood ratio (ARGLR) detectorsby processing the real radar data. We show that the proposed de-tector offers a higher probability of detection than the other tests,for a given probability of falsealarm.