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This paper considers moving target detection (MTD) with distributed multi-input multi-output (MIMO) radars in non-homogeneous environments, where the received disturbance signal (clutter and noise) exhibits non-homogeneity in not only power but also covariance structure from one transmit-receive (TX-RX) antenna pair to another as well as across different test cells. To address this problem, we introduce a parametric approach by employing a set of distinctive auto-regressive (AR) models, one for each TX-RX pair, to model the non-homogeneous disturbance signals. We develop a parametric generalized likelihood ratio test (PGLRT), referred to as the MIMO-PGLRT detector, for MTD in distributed MIMO radars. The MIMO-PGLRT detector, which consists of local adaptive subspace detection, non-coherent combining using local decision variables, and a global threshold comparison, is shown to asymptotically achieve constant false alarm rate (CFAR). We also investigate the target velocity estimation problem, an integral part of MTD, and develop its maximum likelihood estimator. The Cramér-Rao bound, in both the exact and asymptotic forms, respectively, is examined to shed additional light to the problem. Numerical results are presented to demonstrate the effectiveness of the proposed methods.
Date of Publication: May1, 2013