Adaptive Range and Doppler Distributed Target Detection in Non-Gaussian Clutter.

This article deals with the detection of range and Doppler distributed targets imbedded in non-Gaussian clutter. The clutter is modeled as a spherically invariant random process with unknown texture components and a covariance matrix structure. We also assume a set of secondary signal-free data is available to estimate the correlation properties of the clutter. Moreover, the target signal at each range cell is assumed to be a sum of returns from an unknown number of scattering centers (SCs) with unknown amplitudes and Doppler frequencies. A generalized likelihood ratio test based on adaptive Doppler steering matrix estimation is proposed in this work. The detector assumes that the target SCs over the range bins are sparse and exploits a sparse Bayesian optimization model to estimate the unknown Doppler steering matrix. In addition, an adaptive iterative algorithm is proposed to solve the estimation problem. The performance assessment conducted by Monte Carlo simulation confirms the robustness and effectiveness of the proposed detector.