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

Minh Trinh-Hoang; Mohammed Nabil El Korso; Marius Pesavento

ICASSP 2021accepted0 citations

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

Minh Trinh-Hoang, Mohammed Nabil El Korso, Marius Pesavento

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.

BibTeX

@inproceedings{icassp2021_apartiallyrelaxe,
  title = {A Partially-Relaxed Robust DOA Estimator Under Non-Gaussian Low-Rank Interference and Noise},
  author = {Minh Trinh-Hoang and Mohammed Nabil El Korso and Marius Pesavento},
  booktitle = {ICASSP 2021},
  year = {2021}
}