Robust Detection for Mills Cross Sonar

Abstract

Multiarray systems are widely used in sonar and radar applications. They can improve communication speeds, target discrimination, and imaging. In the case of a multibeam sonar system that can operate two receiving arrays, we derive new adaptive tests to improve detection capabilities compared to traditional sonar detection approaches. To do so, we more specifically consider correlated arrays, whose covariance matrices are estimated up to scale factors, and an impulsive clutter. In a partially homogeneous environment, the two-step generalized likelihood ratio test and Rao approach lead to a generalization of the adaptive normalized matched filter test and an equivalent numerically simpler detector with a well-established texture constant false alarm rate behavior. Performances are discussed and illustrated with theoretical examples, numerous simulations, and insights into experimental data. Results show that these detectors outperform their competitors and have stronger robustness to environmental unknowns.