Adaptive strategies for clutter edge detection in radar

Abstract

In this paper, the problem of the detection and localization of clutter edges within training data is addressed. This is accomplished through a procedure capable of discriminating between either a unique homogeneous set or two heterogeneous subsets within a sliding window moving over the set of range bins of interest. The problem is first formulated as a binary hypothesis test assuming that the rank of the covariance clutter component is known and solved resorting to the generalized likelihood ratio test. Then, in the case of no a priori knowledge about the rank of the clutter covariance matrix, a preliminary estimation stage relying on the model order selection rules is devised. Interestingly, the estimates provided by the detection stage can be processed by a fusion algorithm in order to improve the quality of the location estimate of the clutter edge. Finally, the performance analysis conducted in comparison with a suitable competitor highlights the effectiveness of the proposed solutions.