A sparsity based GLRT for moving target detection in distributed MIMO radar on moving platforms

This paper examines moving target detection (MTD) in distributed multi-input multi-output (MIMO) radar with sensors placed on moving platforms. Unlike previous works which were focused on the case of stationary platforms, we consider explicitly the effects of platform motion, which exacerbate the location-induced clutter non-homogeneity inherent in such systems and thus make the MTD problem significantly more challenging. We propose a sparsity based detector which, by exploiting a sparse representation of the clutter in the Doppler domain, adaptively estimates from the test signal the clutter subspace, which is allowed to be distinct for different transmit/receive (Tx/Rx) pairs and, moreover, may spread over the entire Doppler bandwidth. The proposed detector requires no range training signals that are indispensable for conventional detectors but are difficult to obtain in practice. Numerical results indicate that the proposed range training-free detector offers improved detection performance over covariance matrix based detectors when the latter are provided with a moderate amount of training signals.