MIMO radar detection and adaptive design in compound-Gaussian clutter

Multiple-input multiple-output (MIMO) radars with widely separated transmitters and receivers are useful to discriminate a target from clutter using the spatial diversity of the scatterers in the illuminated scene. We consider the detection of targets in compound-Gaussian clutter. Compound-Gaussian clutter describes heavy-tailed distributions fitting high-resolution and/or low-grazing-angle radars in the presence of sea or foliage clutter. First, we introduce a data model using the inverse gamma distribution to represent the clutter texture. Then, we apply the parameter-expanded expectation-maximization (PX-EM) algorithm to estimate the clutter texture and speckle as well as the target parameters. We develop a statistical decision test using these estimates and approximate its statistical characteristics. Based on the approximation of the statistical characteristics of this test, we propose an algorithm to adaptively distribute the total transmitted energy among the transmitters. We demonstrate the advantages of MIMO and adaptive energy allocation using Monte Carlo simulations.