Adaptive array detection algorithms with steering vector mismatch

The problem of signals detection with known templates in coloured Gaussian interference is especially relevant for radar applications. The optimum detector requires knowledge of the true interference covariance matrix, which is usually unknown. In practice, the matrix is estimated from target-free training data that must be statistically homogeneous with the test data. Traditional approaches, like the GLRT and AMF, require the availability of such training data. These conditions are normally not always satisfied, which degrades the detection performance. Recently, two single data set approaches that deal only with the test data, namely the GMLED and MLED were introduced. In this paper, we examine the performance of these detection algorithms and their reduced-dimension counterparts under steering vector mismatches. We investigate the cases where there is a mismatch in the spatial and temporal steering vectors.