Year: 2024
Type: conference-paper
Abstract: This paper considers the passive detection of a signal common to two multi-sensor arrays. We consider Gaussian received signals and noises with positive-definite, but otherwise unstructured covariance matrices. Under the null hypothesis, the composite covariance matrix for the two arrays is block-diagonal with arbitrary positive definite (PD) blocks, whereas under the alternative, it is modeled as an unstructured covariance matrix. Assuming complex inverse-Wishart priors for the unknown covariance matrices, the proposed test relies on the marginalized likelihood ratio, where the unknown parameters (i.e., the covariance matrices) are integrated out. A proper choice of hyper-parameters of the prior distribution shows that the Bayesian-inspired test reduces to a regularized canonical correlation analysis (CCA) detector. Simulation results show the superior performance of the proposed method compared to the generalized likelihood ratio test (GLRT), which is given by a function of the canonical correlations.
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Awards: G1175115578, G1430692297, G1523888516, G210041235, G2262748287, G2390397592, G2410693562, G2601397997, G2620914175, G300979063, G34346518, G3480869486, G3681454997, G4042783231, G425668148, G4516736450, G4720003262, G5080475149, G5195188938, G5551736029, G586857820, G6098521345, G7084143925, G8275470645, G8876996369, G8938627179
OpenAlex ID: W4409156745