Adaptive detection of moving target with MIMO radar in heterogeneous environments based on Rao and Wald tests

• We study the adaptive detection of moving target with MIMO radar in heterogeneous clutter.
• Two novel detectors based on Rao and Wald criteria are developed according to the ad hoc design procedure.
• Covariance matrix estimation via geometric barycenter is proposed using heterogeneous secondary data.
• Simulations reveal that the proposed algorithms outperform some available ones.

This paper deals with the adaptive detection of moving targets for multiple-input multiple-output (MIMO) radar in heterogeneous clutter environments. Two new detectors based on Rao and Wald criteria are developed using an ad hoc design procedure. Precisely, we first obtain the Rao and Wald tests by assuming the known target velocity and the known structure of the clutter. Then, we modify them by performing a numerical optimization with respect to the target velocity and replacing the clutter covariance matrix with a proper estimate. With a limited number of secondary data set, a class of covariance matrix estimators, defined as the geometric barycenters of some basic covariance matrix estimates obtained from the available secondary data set, are proposed by exploiting the characteristic of the positive-definite matrix space. Finally, numerical results are presented to demonstrate the effectiveness of the proposed detectors and covariance matrix estimators.