Adaptive Radar Detection in the Presence of Missing-data

This article deals with the problem of adaptive radar detection in a missing-data context, where the complete observations (i.e., downstream information loss mechanisms) are characterized by homogeneous Gaussian disturbance with an unknown but possibly structured covariance matrix. The detection pro...

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Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems Vol. 58; no. 4; pp. 3283 - 3296
Main Authors: Aubry, Augusto, Carotenuto, Vincenzo, De Maio, Antonio, Rosamilia, Massimo, Marano, Stefano
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive radar detection
Algorithms
Covariance matrices
Covariance matrix
Detectors
expectation–maximization (EM) algorithm
Hypotheses
Interference
Likelihood ratio
Maximization
Maximum likelihood estimates
Maximum likelihood estimation
Missing data
Optimization
Radar
Radar detection
ISSN:0018-9251, 1557-9603
Online Access:Get full text
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Summary:This article deals with the problem of adaptive radar detection in a missing-data context, where the complete observations (i.e., downstream information loss mechanisms) are characterized by homogeneous Gaussian disturbance with an unknown but possibly structured covariance matrix. The detection problem, formulated as a composite hypothesis test, is tackled by resorting to suboptimal design strategies, leveraging the generalized likelihood ratio criterion demanding appropriate maximum likelihood estimates (MLEs) of the unknowns under both hypotheses. Capitalizing on some possible a priori knowledge about the interference covariance matrix structure, the optimization problems involved in the MLE computation are handled by employing the expectation-maximization (EM) algorithm or its expectation-conditional maximization and multicycle EM variants. At the analysis stage, the performance of the devised architectures is assessed both via Monte Carlo simulations and on measured data for some covariance matrix structures of practical interest.
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ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2022.3147443