MAXIMUM-LIKELIHOOD METHODS IN RADAR ARRAY SIGNAL-PROCESSING

We consider robust and computationally efficient maximum likelihood al gorithms for estimating the parameters of a radar target whose signal is observed by an array of sensors in interference with unknown second -order statistics. Two data models are described: one that uses the ta rget direction of arrival and signal amplitude as parameters and one t hat is a simpler, unstructured model that uses a generic target ''spat ial signature.'' An extended invariance principle is invoked to show h ow the less accurate be refined to achieve asymptotically the performa nce available using the structured model. The resulting algorithm requ ires Two one-dimensional (1-D) searches rather than a two-dimensional search, as with previous approaches for the structured case. If a unif orm linear array is used, only a single 1-D search is needed. A genera lized likelihood ratio test for target detection is also derived under the unstructured model. The principal advantage of this approach is t hat it is computationally simple and robust to errors in the model (ca libration) of the array response.