Covariance recovery for one-bit sampled stationary signals with time-varying sampling thresholds

A Eamaz, F Yeganegi, M Soltanalian - Signal Processing, 2023 - Elsevier
One-bit quantization, which relies on comparing the signals of interest with given threshold
levels, has attracted considerable attention in signal processing for communications and …

Sparse Bayesian learning approach for outlier-resistant direction-of-arrival estimation

J Dai, HC So - IEEE Transactions on Signal Processing, 2017 - ieeexplore.ieee.org
Conventional direction-of-arrival (DOA) estimation methods are sensitive to outlier
measurements. Therefore, their performance may degrade substantially in the presence of …

On multiple covariance equality testing with application to SAR change detection

D Ciuonzo, V Carotenuto… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
This paper deals with the problem of testing the equality of M covariance matrices. We first
identify a suitable group of transformations leaving the problem invariant and obtain the …

Generalized robust shrinkage estimator and its application to STAP detection problem

F Pascal, Y Chitour, Y Quek - IEEE Transactions on Signal …, 2014 - ieeexplore.ieee.org
Recently, in the context of covariance matrix estimation, in order to improve as well as to
regularize the performance of the Tyler's estimator also called the Fixed-Point Estimator …

Robust adaptive detection of buried pipes using GPR

Q Hoarau, G Ginolhac, AM Atto, JM Nicolas - Signal Processing, 2017 - Elsevier
Detection of buried objects such as pipes using a Ground Penetrating Radar (GPR) is
intricate for three main reasons. First, noise is important in the resulting image because of …

Robust sparse Bayesian learning for DOA estimation in impulsive noise environments

R Zheng, X Xu, Z Ye, J Dai - Signal Processing, 2020 - Elsevier
Conventional direction of arrival (DOA) estimation methods are derived under Gaussian
distributional assumptions on the noise and inevitably induce undesirable biases in …

DOA M-estimation using sparse Bayesian learning

CF Mecklenbräuker, P Gerstoft… - ICASSP 2022-2022 IEEE …, 2022 - ieeexplore.ieee.org
Recent investigations indicate that Sparse Bayesian Learning (SBL) is lacking in
robustness. We derive a robust and sparse Direction of Arrival (DOA) estimation framework …

Structured robust covariance estimation

A Wiesel, T Zhang - Foundations and Trends® in Signal …, 2015 - nowpublishers.com
We consider robust covariance estimation with an emphasis on Tyler's M-estimator. This
method provides accurate inference of an unknown covariance in non-standard settings …

Highly robust complex covariance estimators with applications to sensor array processing

JA Fishbone, L Mili - IEEE Open Journal of Signal Processing, 2023 - ieeexplore.ieee.org
Many applications in signal processing require the estimation of mean and covariance
matrices of multivariate complex-valued data. Often, the data are non-Gaussian and are …

Robust variational Bayesian inference for direction-of-arrival estimation with sparse array

Y Liu, Z Zhang, C Zhou, C Yan… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Conventional direction-of-arrival (DOA) estimation algorithms are sensitive to array
imperfections and outliers, making it challenging to realize accurate estimates in real …