Iterative marginal maximum likelihood DOD and DOA estimation for MIMO radar in the presence of SIRP clutter

Abstract

The spherically invariant random process (SIRP) clutter model is commonly used in scenarios where the radar clutter cannot be correctly modeled as a Gaussian process. In this short communication, we devise a novel Maximum-Likelihood (ML)-based iterative estimator for direction-of-departure and direction-of-arrival estimation in the Multiple-input multiple-output (MIMO) radar context in the presence of SIRP clutter. The proposed estimator employs a stepwise numerical concentration approach w.r.t. the objective function related to the marginal likelihood of the observation data. Our estimator leads to superior performance, as our simulations show, w.r.t. to the existing likelihood based methods, namely, the conventional, the conditional and the joint likelihood based estimators, and w.r.t. the robust subspace decomposition based methods. Finally, interconnections and comparison between the Iterative Marginal ML Estimator (IMMLE), Iterative Joint ML Estimator (IJMLE) and Iterative Conditional ML Estimator (ICdMLE) are provided.

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

“…For estimation of the aforementioned targets' parameters in the non-Gaussian clutter environment, various variants of maximum likelihood estimator (MLE) are proposed in [13], [14], and [15]. However, the estimation of DOD, DOA, and Doppler shift, in the presence of non-Gaussian clutter, does not have a closed-form solution for optimization of MLE cost function [16].…”

“…The MLE based solution, proposed in [13], and [14] are based on the conditional likelihood, and the joint likelihood of the observations, respectively. Later, in [15], it is mentioned and shown that the estimators proposed in [13], and [14] are prone to yield suboptimum estimates of required parameters. Therefore in [15], an iterative ML estimator has been proposed.…”

“…Later, in [15], it is mentioned and shown that the estimators proposed in [13], and [14] are prone to yield suboptimum estimates of required parameters. Therefore in [15], an iterative ML estimator has been proposed. The proposed estimator is based on the marginal likelihood of the observations and claimed to perform better than the estimators proposed in [13], and [14].…”

“…The proposed estimator is based on the marginal likelihood of the observations and claimed to perform better than the estimators proposed in [13], and [14]. However, in [15], as the marginal likelihood of the observation is considered, the final estimate depends on the numerical evaluation of integrals, which is computationally demanding. In addition to need for evaluation of integrals, as mentioned in Section 3, Remark 4 of [15], for say, P targets, the estimate of DOD/DOA, is obtained by performing the 2P dimensional grid search algorithm of a highly non-convex function.…”

“…However, in [15], as the marginal likelihood of the observation is considered, the final estimate depends on the numerical evaluation of integrals, which is computationally demanding. In addition to need for evaluation of integrals, as mentioned in Section 3, Remark 4 of [15], for say, P targets, the estimate of DOD/DOA, is obtained by performing the 2P dimensional grid search algorithm of a highly non-convex function. Implementing a grid search algorithm over 2P dimension is computationally complex.…”