LAUSR

This paper addresses the problem of small slow target detection in a strong clutter. By exploiting the characteristics of radar cross section fluctuation, the complex Gaussian procedure is utilized to model the matching filter output, introduce the Euclidean distance between the probability density functions (PDF-ED) to represent the resolvability of different hypotheses, and propose an adaptive waveform optimization algorithm that maximizes the PDF-ED between different hypotheses. Compared to the traditional ED between hypotheses, which only takes the mean value of the probability density into consideration, the PDF-ED metric considers the shape of the PDF curve and specifies the essential difference between two hypotheses in terms of information geometry. In this way, the proposed algorithm enhances the target and suppresses the clutter by reducing the side lobes at specified delay-Doppler units, resulting in the improvement of detection performance. The numerical experiments validate the effectiveness of the algorithm.