Constrained adaptive detection of range spread targets

In this paper we consider the problem of detecting range spread targets in the presence of steering vector mismatches and Gaussian disturbance with unknown covariance matrix. To this end we resort to the Generalized Likelihood Ratio (GLR) principle and devise several detectors accounting for possible mismatches between the actual and the nominal steering vector. At the analysis stage, we assess the performance of the new algorithms, both in the matched and mismatched signal cases, also in comparison with a previously proposed GLR Test (GLRT) designed assuming that the nominal and the actual steering vectors coincide. The results show that the new tests may achieve performance improvements over the previously proposed GLRT when the mismatch is present. Moreover they exhibit an acceptable performance loss in the matched signal case.