Knowledge-Aided Wideband Radar Target Detection in the Heterogeneous Clutter

In this article, the problem of wideband radar target detection in a heterogeneous environment is addressed. The wideband radar return of the target with range migration is characterized as a subband model, and the heterogeneous clutter is described with a hierarchical Bayesian model. Both the prior knowledge of the clutter power and the covariance matrix and the dependence of the primary data and the secondary data are characterized by the inverse gamma and the inverse complex Wishart distribution, respectively. Based on the target and the clutter models, knowledge-aided maximum posterior ratio test, knowledge-aided Rao test, and knowledge-aided Wald test for wideband radar target detection in heterogeneous clutter are proposed. Finally, the performance of the proposed detectors is evaluated by simulations with both the simulated clutter generated by the probability model and the synthesized clutter from a real synthetic aperture radar complex image. The results show that the proposed knowledge-aided detectors are effective for wideband radar target detection in heterogeneous clutter.