Adaptive detection of range-spread targets by the generalized detector

In this paper, we address an adaptive detection of range-spread targets or targets embedded in Gaussian noise with unknown covariance matrix by the generalized detector (GD) based on the generalized approach to signal processing (GASP) in noise. We assume that cells or secondary data that are free of signal components are available. Those secondary data are supposed to process either the same covariance matrix or the same structure of the covariance matrix of the cells under test. In this context, under designing GD we use a two-step procedure. The criteria lead to receivers ensuring the constant false alarm rate (CFAR) property with respect to unknown quantities. A thorough performance assessment of the proposed detection strategies highlights that the two-step design procedure of decision-making rule in accordance with GASP is to be preferred with respect to the plain one. In fact, the proposed design procedure leads to GD that achieves significant improvement in detection performance under several situation of practical interest. For estimation purposes, we resort to a set of secondary data. In addition to the classical homogeneous scenario, we consider the case wherein the power value of primary and secondary data vectors is not the same. The design of adaptive detection algorithms based on GASP in the case of mismatch is a problem of primary concern for radar applications. We demonstrate that two-step design procedure based on GASP ensures minimal loss.