Adaptive subspace detection of range-distributed target in compound-Gaussian clutter
Digital Signal Processing
CFAR detection strategies for distributed targets under conic constraints
IEEE Transactions on Signal Processing
Adaptive detection and estimation in the presence of useful signal and interference mismatches
IEEE Transactions on Signal Processing
Detection performance analysis of tests for spread targets in compound-gaussian clutter
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
Digital Signal Processing
Persymmetric adaptive detection of distributed targets in partially-homogeneous environment
Digital Signal Processing
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This paper addresses adaptive radar detection of distributed targets in noise plus interference assumed to belong to a known or unknown subspace of the observables. At the design stage we resort to either the GLRT or the so-called two-step GLRT-based design procedure and assume that a set of noise-only data is available (the so-called secondary data). Detection algorithms have been derived modeling noise vectors, corresponding to different range cells, as independent, zero-mean, complex normal ones, sharing either the same covariance matrix (homogeneous environment) or the same covariance matrix up to possibly different (mean) power levels between primary data, i.e., range cells under test, and secondary ones (partially homogeneous environment). The performance assessment has been conducted by Monte Carlo simulation, also in comparison to previously proposed detection algorithms, and confirms the effectiveness of the newly proposed ones