Regularized Tapered Sample Covariance Matrix

Covariance matrix tapers have a long history in signal processing and related fields. Examples of applications include autoregressive models (promoting a banded structure) or beam-forming (widening the spectral null width associated with an interferer). In this paper, the focus is on high-dimensional setting where the dimension p is high, while the data aspect ratio n/p is low. We propose an estimator called TABASCO (TApered or BAnded Shrinkage COvariance matrix) that shrinks the tapered sample covariance matrix towards a scaled identity matrix. We derive optimal and estimated (data adaptive) regularization parameters that are designed to minimize the mean squared error (MSE) between the proposed shrinkage estimator and the true covariance matrix. These parameters are derived under the general assumption that the data is sampled from an unspecified elliptically symmetric distribution with finite 4th order moments (both real-and complex-valued cases are addressed). Simulation studies show that the proposed TABASCO outperforms all competing tapering covariance matrix estimators in diverse setups. An application to space-time adaptive processing (STAP) also illustrates the benefit of the proposed estimator in a practical signal processing setup.

Automated summary

This paper investigates the problem of covariance matrix estimation in high-dimensional settings and proposes a new estimator called TABASCO. By shrinking the tapered sample covariance matrix, more accurate estimation results can be obtained. Simulation studies and practical application results demonstrate that TABASCO outperforms other competing estimators in various scenarios.