Maximum likelihood methods in radar array signal processing

We consider robust and computationally efficient maximum likelihood algorithms 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 target direction of arrival and signal amplitude as parameters and one that is a simpler, unstructured model that uses a generic target "spatial signature". An extended invariance principle is invoked to show how the less accurate maximum likelihood estimates obtained from the simple model may be refined to achieve asymptotically the performance available using the structured model. The resulting algorithm requires two one-dimensional (1-D) searches rather than a two-dimensional search, as with previous approaches for the structured case. If a uniform linear array is used, only a single 1-D search is needed. A generalized likelihood ratio test for target detection is also derived under the unstructured model. The principal advantage of this approach is that it is computationally simple and robust to errors in the model (calibration) of the array response.