A Partially-Relaxed Robust DOA Estimator Under Non-Gaussian Low-Rank Interference and Noise

Abstract

In practical applications, non-Gaussianity of the signal at the sensor array is detrimental to the performance of conventional Direction-of-Arrival (DOA) estimators developed under the Gaussian model. In this paper, we propose a novel robust DOA estimator from the data collected at the sensor array under the corruption of non-Gaussian interference and noise. Additionally, the Cramér-Rao bound for DOA parameters under the considered signal model is derived. Simulation results show that the proposed estimator exhibits near-optimal estimation performance under the assumed model while being robust to model mismatch and/or the presence of outliers.

Neural Network-Based DOA Estimation in the Presence of Non-Gaussian Interference

“…Notice that the proposed DAFC-based NN mitigates interference, while the spectra of other tested approaches contain high peaks at the interference DOA, θ c . These peaks increase the Hausdorff distance in (17), increasing the RMSD of other tested approaches in Fig. 8.…”

“…Therefore, [8] proposed a kernel minimum error entropy-based adaptive estimator and a novel criterion to reduce the estimator's computational complexity. The expectation-maximization (EM) with a partial relaxationbased DOA estimation algorithm under the conditional model assumption was proposed in [17]. In [18] a sparse Bayesian learning (SBL) approach for outlier rejection of impulsive and spatially-white interference was proposed.…”