Multiple-input multiple-output radar detectors design in non-Gaussian clutter

This study mainly deals with the problem of multiple-input multiple-output (MIMO) radar in compound-Gaussian clutter-dominated scenario with unknown covariance matrix. The general MIMO model, with widely separated sub-arrays and co-located antennas at each sub-array, is adopted. First, the signal model is developed to compound-Gaussian clutter, and then, the generalised likelihood rate test (GLRT) and Rao detectors are derived with known covariance matrix. To make the detectors fully adaptive, the secondary with signal-free data is collected to estimate the covariance. A thorough performance assessment is also given, which considers the sense with co-located antennas configuration of transmitters and receivers array. Via several numerical examples, it is shown that the derived GLRT and Rao detectors can provide excellent detection performance in spikier clutter compared with the detector, which is derived by Jian Li in Gaussian clutter. To this end, the Rao detector outperforms best.