Optimum Scatterer Estimation and Adaptive Detection of Sparse Range-spread Target

Abstract

This paper addresses sparse range-spread target detection in non-Gaussian clutter model as spherically invariant random vector. An optimum scatterers integrator (OSI) is proposed for the problem that it is hard to accurately estimate the number of scatters of the spare range-spread target. The OSI is obtained by integrating the energy of strong scatterers estimated based on the method of maximum detection probability. Moreover, the covariance matrix structure is estimated based on the clutter-cluster estimation and the technology of recursive estimation. An adaptive OSI (AOSI) is obtained by replacing the covariance matrix structure with the estimated one. The AOSI has the Constant false alarm ratio (CFAR) property with respect to the covariance matrix structure and the texture component of the clutter. Finally, the simulation results show the validity of proposed methods.