In this work we derive the maximum likelihood estimator for passive wavefront curvature ranging systems operating in environments subject to a spatial coherence loss. As a consequence of the spatial coherence loss, the optimum processor is no longer a rank-1 matched filter and now instead involves a multi-rank weighted combination of the data based on the coherence matrix eigenvectors and eigenvalues. We also establish an interesting connection of our proposed multi-rank processor to the conventional rank-1 processor, and to the non-coherence sub-array processor, under different operating conditions. A comparative study is carried out in evaluating the performance of the proposed processors.
Hongya Ge, Ivars P. Kirsteins