Suppression of Non-Gaussian Clutter from Subspace Interference

Abstract

In complex electromagnetic environments, a clutter covariance matrix is required to estimate in the on-line manner, so as to adaptively adjust the filter weight to effectively suppress clutter, thereby improving target estimation, detection, location, and tracking. In this paper, a non-Gaussian clutter model is considered, while apart of the clutter data maybe contaminated by subspace interference, wherein the signal of interest is located in the subspace. To this end, we propose a knowledge-aided hierarchical Bayesian model and obtain the approximated posterior distribution of the clutter covariance matrix by exploiting variational Bayesian inference methods. The target detection performance can be enhanced using a clutter-suppression filter that is designed based on the posterior mean of the clutter covariance matrix. A comparison of the computer simulation results with real clutter data confirms that the proposed method can suppress the clutter and improve detection performance.

Keywords

• non-gaussian clutter
• subspace interference
• hierarchical bayesian model
• variational bayesian inference
• clutter suppression