This paper deals with the problem of adaptive radar detection in a context with missing-data 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 sub-optimal design strategies, leveraging the generalized likelihood ratio (GLR) 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 MLEs computation are handled by employing the expectation-maximization (EM) algorithm or its expectation-conditional maximization (ECM) and multi-cycle EM (M-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.

Adaptive Radar Detection in the Presence of Missing-Data

Marano S.
2022-01-01

Abstract

This paper deals with the problem of adaptive radar detection in a context with missing-data 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 sub-optimal design strategies, leveraging the generalized likelihood ratio (GLR) 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 MLEs computation are handled by employing the expectation-maximization (EM) algorithm or its expectation-conditional maximization (ECM) and multi-cycle EM (M-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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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4782434
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