Clutter Identification Based On Sparse Recovery And L1-Type Probabilistic Distance Measures

Cognitive radar framework has recently been proposed in radar signal processing to develope algorithms for target detection, tracking, and waveform design in the presence of nonstationary environmental (clutter) characteristics. In this framework, there are the three main steps: sensing the environmental changes, learning the new environmental statistical characteristics, and adapting the radar algorithms to the new characteristics. Here, we focus on the second step of the framework to identify the new clutter characteristics after a change is detected in the environment. We form a dictionary of various clutter distributions and identify the distribution of the new clutter data through matching pursuit using probabilistic similarity and distance measures under sparsity constraints. Specifically, we use inner-product as a similarity measure, and we apply three different L1-norm type probabilistic distance measures. We both numerically and analytically analyze their clutter-distribution identification performances and show that Kulczynski is the best distance measure for distribution identification.