Kernel Minimum Error Entropy Based Estimator for MIMO Radar in Non-Gaussian Clutter

In this paper, a kernel minimum error entropy (KMEE) based estimator is proposed for the estimation of multiple targets' direction of departure (DOD), the direction of arrival (DOA), and the Doppler shift with multiple input multiple output radar in the presence of non-Gaussian clutter. Most existing estimation approaches are based on optimization of a complex cost function which often leads to a sub-optimum solution. Therefore, for the accurate estimation of DOD, DOA and Doppler shift, an efficient, kernel adaptive filter (KAF) based estimation approach is proposed. The proposed estimator utilizes the minimum error entropy (MEE) criterion and minimizes the error entropy function. The MEE, being an information-theoretic criterion, optimizes the higher-order statistics of error and thus makes the proposed estimator robust against the effects of outliers like clutter. The KMEE based estimator without any sparsification suffers from a linear increase in computational complexity. Thus, subsequently, the computational complexity of the proposed KMEE based estimator is reduced by incorporation of novelty criterion (NC) based sparsification technique, and the resulting estimator is called KMEE-NC. The performance of the proposed KMEE-NC based estimator is compared with the recently introduced sparse estimators based on kernel maximum correntropy criterion, and kernel minimum mean square error criterion. Additionally, KMEE-NC based estimator is also compared with other existing conventional estimators. Further, for assessing the accuracy of the proposed estimator, the modified Cramer-Rao lower bound is derived using the modified Fisher information matrix.