Title :
Adaptive Radar Detection: A Bayesian Approach
Author :
De Maio, Antonio ; Farina, Alfonso
Author_Institution :
Univ. degli Studi di Napoli Federico II, Naples
Abstract :
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.
Keywords :
Bayes methods; adaptive radar; adaptive signal detection; covariance matrices; decision theory; probability; radar detection; Bayesian approach; Gaussian disturbance; adaptive radar detection; decision rules; generalized likelihood ratio test; nonBayesian GLRT detector; probability density function; unknown disturbance covariance matrix; Bayesian methods; Covariance matrix; Detectors; Equations; Probability density function; Radar applications; Radar clutter; Radar detection; Testing; Training data;
Conference_Titel :
Radar Symposium, 2006. IRS 2006. International
Conference_Location :
Krakow
Print_ISBN :
978-83-7207-621-2
DOI :
10.1109/IRS.2006.4338007