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Adaptive Radar Detection: A Bayesian Approach

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2 Author(s)
De Maio, Antonio ; Univ. degli Studi di Napoli Federico II, Naples ; Farina, Alfonso

In this paper we consider the problem of adaptive radar detection in Gaussian disturbance with unknown spectral properties. To this end we resort to a Bayesian approach based on a suitable model for the probability density function of the unknown disturbance covariance matrix. We devise two detectors based on the generalized likelihood ratio test (GLRT) criterion both one-step and two-step. The new decision rules achieve a better performance level than some conventional radar detectors in the presence of heterogeneous scenarios, where a small number of training data is available. Finally they ensure the same performance of the non Bayesian GLRT detectors when the size of the training set is sufficiently large.

Published in:

Radar Symposium, 2006. IRS 2006. International

Date of Conference:

24-26 May 2006