Adaptive Radar Detection in joint Range-Azimuth Space based on the Hierarchical Latent Variable Model

This paper focuses on the design of a robust decision scheme capable of operating in target-rich scenarios with unknown signatures (including their range positions, angles of arrival, and number) in a background of Gaussian disturbance. To solve the problem at hand, an estimation procedure is conceived by resorting to the expectation-maximization algorithm in conjunction with the hierarchical latent variable model that are exploited to come up with a maximum a posteriori rule for reliable signal classification and angle of arrival estimation. The estimates returned by the procedure are then used to build up an adaptive detection architecture based on the likelihood ratio test. Remarkably, it is shown that such a decision scheme can maintain the false alarm rate constant when the interference parameters vary in the considered range of values. The performance assessment, conducted by means of Monte Carlo simulation, highlights that the proposed detector exhibits superior detection performance in comparison with the existing GLRT-based competitors.