Adaptive Detection and Steering Estimation in the Presence of Useful Signal and Interference Mismatches

This paper considers adaptive detection and steering estimation in the presence of useful signal and interference mismatches. We assume a homogeneous environment where the random disturbance components from the primary and secondary data share the same covariance matrix. Moreover the data under test contains a deterministic interference vector in addition to the possible useful signal. We focus on the situation where an energy fraction of both the useful signal and the deterministic interference may lie outside the range span of their nominal directions. Under these conditions, we devise a procedure for the computation of the joint Maximum Likelihood (ML) estimators of the useful signal and interference vectors, resorting to a suitable rank-one decomposition of a Semidefinite Program (SDP) problem optimal solution. Hence we use the aforementioned estimators for the synthesis of adaptive receivers based on different Generalized Likelihood Ratio Test (GLRT) criteria. At the analysis stage we assess the performance of the new detectors also in comparison with two decision rules, available in open literature, which assume the useful signal and interference components perfectly aligned with their nominal directions.