Convergence of Structured Quadratic Forms With Application to Theoretical Performances of Adaptive Filters in Low Rank Gaussian Context

TL;DR

This paper develops a theoretical framework using Random Matrix Theory to predict the performance of low-rank adaptive filters in high-dimensional Gaussian noise environments, relaxing previous restrictive assumptions.

Contribution

It introduces new RMT-based convergence results for structured quadratic forms, enabling accurate SINR loss prediction without restrictive orthogonality assumptions.

Findings

Theoretical convergence of structured quadratic forms in large dimensions.

Improved SINR loss prediction accuracy in non-orthogonal scenarios.

Validation through simulations showing advantages over previous methods.

Abstract

This paper addresses the problem of deriving the asymptotic performance of adaptive Low Rank (LR) filters used in target detection embedded in a disturbance composed of a LR Gaussian noise plus a white Gaussian noise. In this context, we use the Signal to Interference to Noise Ratio (SINR) loss as performance measure which is a function of the estimated projector onto the LR noise subspace. However, although the SINR loss can be determined through Monte-Carlo simulations or real data, this process remains quite time consuming. Thus, this paper proposes to predict the SINR loss behavior in order to not depend on the data anymore and be quicker. To derive this theoretical result, previous works used a restrictive hypothesis assuming that the target is orthogonal to the LR noise. In this paper, we propose to derive this theoretical performance by relaxing this hypothesis and using Random Matrix Theory (RMT) tools. These tools will be used to present the convergences of simple quadratic forms and perform new RMT convergences of structured quadratic forms and SINR loss in the large dimensional regime, i.e. the size and the number of the data tend to infinity at the same rate. We show through simulations the interest of our approach compared to the previous works when the restrictive hypothesis is no longer verified.