We address the problem of adaptive radar detection of point-like targets in presence of Gaussian noise with unknown spectral properties, adopting a “second-order approach” to target modeling. In fact, the presence of a useful signal is modeled in terms of a rank-1 modification of the noise covariance matrix, similarly for the possible presence of a fictitious signal under the null hypothesis, aimed at increasing the selectivity of the detector. A set of homogeneous training data is assumed to be available. Results show that the proposed detectors can outperform natural competitors, especially assuming a limited number of training data.
Adaptive Radar Detectors for Point-Like Gaussian Targets in Gaussian Noise
IEEE Transactions on Aerospace and Electronic Systems ; 53 , 3 ; 1284-1294
2017-06-01
824649 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
CFAR Detectors for High Resolution Radar in Non-Gaussian Clutter
British Library Conference Proceedings | 1997
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