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 …

Conventional direction-of-arrival (DOA) estimation methods are sensitive to outlier
measurements. Therefore, their performance may degrade substantially in the presence of …

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 …

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 …

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 …

Conventional direction of arrival (DOA) estimation methods are derived under Gaussian
distributional assumptions on the noise and inevitably induce undesirable biases in …

Recent investigations indicate that Sparse Bayesian Learning (SBL) is lacking in
robustness. We derive a robust and sparse Direction of Arrival (DOA) estimation framework …

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 …

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 …

Conventional direction-of-arrival (DOA) estimation algorithms are sensitive to array
imperfections and outliers, making it challenging to realize accurate estimates in real …