Doppler spectrum segmentation of radar sea clutter by mean-shift and information geometry metric

Abstract

Radar sea clutter inhomogeneity in range is characterized by Doppler mean and spectrum width variations. We propose a new approach for robust statistical density estimation and segmentation of sea clutter Doppler spectrum. In each range cell, Doppler is characterized by a Toeplitz Hermitian Positive Definite covariance matrix that is coded in Poincaré's unit poly-disk and we use adaptation of standard kernel methods to density estimation on this specific Riemannian manifold. Based on this non-parametric approach to estimate statistical density of Doppler Spectrum, we address the problem of sea clutter data mapping and segmentation by extending "Mean-Shift" tool for these densities on Poincaré's unit poly-disk. This statistical segmentation is requested for robust detection of targets in sea clutter, especially in case of high sea state.