The covariance matrix (or its inverse, the precision matrix) is central to many chemometric
techniques. Traditional sample estimators perform poorly for high‐dimensional data such as …

Frequency diverse array multiple-input multiple-output (FDA-MIMO) radar produces an
angle-range-dependent and time-varying transmit beampattern due to the small frequency …

In this paper, we propose a class of median-based matrix information geometry (MIG)
detectors with a manifold filter and apply them to signal detection in nonhomogeneous …

Covariance matrix estimation is problematic when the number of samples is relatively small
compared with the number of variables. One way to tackle this problem is through the use of …

Shrinkage can effectively improve the condition number and accuracy of covariance matrix
estimation, especially for low-sample-support applications with the number of training …

The ensemble Gaussian mixture filter (EnGMF) combines the simplicity and power of
Gaussian mixture models with the provable convergence and power of particle filters. The …

Linear discriminant analysis (LDA) is a widely used technique for data classification. The
method offers adequate performance in many classification problems, but it becomes …

Covariance matrix tapers have a long history in signal processing and related fields.
Examples of applications include autoregressive models (promoting a banded structure) or …

The penalized covariance estimator has been widely used for estimating large sparse
covariance matrices. It is recognized that penalty introduces a non-negligible estimation …

This paper develops efficient ensemble Kalman filter (EnKF) implementations based on
shrinkage covariance estimation. The forecast ensemble members at each step are used to …