Subspace selection for partially adaptive sensor array processing

Abstract

This paper introduces a cross-spectral metric for subspace selection and rank reduction in partially adaptive minimum variance array processing. The counter-intuitive result that it is suboptimal to perform rank reduction via the selection of the subspace formed by the principal eigenvectors of the array covariance matrix is demonstrated. A cross-spectral metric is shown to be the optimal criterion for reduced-rank Wiener filtering.

Reduced-Rank STAP Schemes for Airborne Radar Based on Switched Joint Interpolation, Decimation and Filtering Algorithm

“…The PC method which is also known as the eigencanceller method [4] suggested to form the projection matrix using the eigenvectors of the covariance matrix R corresponding to the eigenvalues with significant magnitude. The CSM method, a counterpart of the PC method belonging to the eigen-decomposition algorithm family, outperforms the PC method because it employs the projection matrix which contains the eigenvectors which contribute the most towards maximizing the SINR [17]. A family of closely related reduced-rank adaptive filters, such as the MSWF [18] and the AVF [19], employs a set of basis vectors as the projection matrix which spans the same subspace, known as the Krylov subspace.…”

MMSE-Based MDL Method for Accurate Source Number Estimation

“…It is worth stressing that the MMDL method does not use the ordered in the MMSE calculation which is the essential difference between the proposed method and the NMDL method although they take similar formulations. Actually, the nonnegative term is the well-known cross-spectral (CS) energy of the cross-spectral metric (CSM) [10] for adaptive reduced-rank filtering. If the CS energy terms are arranged in a decreasing order and then used to calculate the MMSE, one may obtain the faster convergence of the MMSE.…”

Reduced-Rank MMSE Detection in Space-Time Coded Space-Division Multiple-Access Systems

“…The ponents (PC), cross-spectral metric (CSM) and Taylor poly-three classes of reduced-rank MMSE algorithms are derived, nomial approximation (TPA). Our study and simulation re-respectively, based on the principles of PC [4] - [7], CSM [6,8] sults show that the reduced-rank MMSE detection is capable and TPA [9]. The characteristics of these reduced-rank MMSE of achieving a satisfactory trade-off between the affordable de-algorithms are considered, and their BER performance is intection complexity and the achievable detection BER perfor-vestigated by simulation, when communicating over indepenmance.…”