Knowledge-based adaptive detection: Joint exploitation of clutter and system symmetry properties

We address adaptive radar detection of targets embedded in clutter characterized by a symmetrically structublack power spectral density (PSD) and persymmetric covariance matrix. At the design stage, such properties are jointly exploited to come up with decision schemes capable of guaranteeing superior detection performances with respect to architectures which incorporate either persymmetry or clutter PSD symmetry. The performance analysis, both on simulated and on real radar data, confirms the superiority of the newly proposed architectures over their natural counterparts which do not take advantage of both the sources of a priori information

Exploiting multiple a priori spectral models for adaptive radar detection

This study deals with the problem of adaptive radar detection when a limited number of training data, due to environmental heterogeneity, is present. Suppose that some a priori spectral models for the interference in the cell under test and a lower bound on the power spectral density (PSD) of the white disturbance term are available. Hence, generalised likelihood ratio test-based detection algorithms have been devised. At the design stage, the basic idea is to model the actual interference inverse covariance as a combination of the available a priori models and to account for the available lower bound on the PSD. At the analysis stage, the capabilities of the new techniques have been shown to detect targets when few training data are available as well as their superiority with respect to conventional adaptive techniques based on the sample covariance matrix

Diffuse multipath exploitation for adaptive radar detection

We deal with the problem of detecting point-like targets in diffuse multipath environments, modeling the target echo as the superposition of a deterministic signal with an unknown scaling factor (due to the direct path) plus a zero-mean complex circular symmetric Gaussian random vector with an unknown covariance matrix (accounting for the echoes from the glistening surface). We devise a constrained Generalized Likelihood Ratio Test (GLRT) for the resulting hypothesis testing problem, enforcing the primary data covariance matrix (due to both interference and multipath echoes) to belong to a neighborhood of the secondary data sample covariance matrix. Remarkably, the proposed decision scheme ensures the desirable Constant False Alarm Rate (CFAR) property with respect to the unknown parameters of the interference. The performance assessment, conducted on simulated data in terms of detection probability also in comparison with existing solutions, highlights the effectiveness of the new approach to cope with diffuse multipath phenomena