Multiple Subspace-Based Target Detection in Deterministic Interference

Abstract

In this letter, the problem of detecting a multiple subspace-based target in the presence of deterministic interference is considered. To solve the problem, we utilize the Kullback-Leibler information criterion and model order selection rules to design detection schemes. The alternative hypothesis related to the most likely signal subspace is selected from multiple alternative hypotheses, and is tested versus the null hypothesis for target detection. Numerical examples verify the effectiveness of the proposed detection schemes, which can achieve the target detection and subspace-based target classification simultaneously.