Fast converging adaptive matched filter and adaptive cosine/coherence estimator

Abstract

This paper deals with the problem of detecting a signal known up to a scaling factor in the presence of Gaussian disturbance with unknown covariance matrix. We focus on scenarios where the number of secondary data which share the same covariance of the data under test is very limited. Thus, we propose a modified version of the adaptive matched filter (AMF) and of the adaptive cosine/coherence estimator (ACE) which can be applied even if the sample covariance matrix is rank deficient. Even though these detectors do not ensure the constant false alarm rate (CFAR) property with respect to the covariance matrix of the disturbance, a sensitivity analysis has shown that the threshold setting is very robust with respect to possible discrepancies between the design and the operating conditions. Finally, numerical results highlight that the proposed receivers outperform the conventional AMF and ACE in low sample situations.