English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper addresses the problem of deriving the asymptotic performance ofadaptive Low Rank (LR) filters used in target detection embedded in adisturbance composed of a LR Gaussian noise plus a white Gaussian noise. Inthis context, we use the Signal to Interference to Noise Ratio (SINR) loss asperformance measure which is a function of the estimated projector onto the LRnoise subspace. However, although the SINR loss can be determined throughMonte-Carlo simulations or real data, this process remains quite timeconsuming. Thus, this paper proposes to predict the SINR loss behavior in orderto not depend on the data anymore and be quicker. To derive this theoreticalresult, previous works used a restrictive hypothesis assuming that the targetis orthogonal to the LR noise. In this paper, we propose to derive thistheoretical performance by relaxing this hypothesis and using Random MatrixTheory (RMT) tools. These tools will be used to present the convergences ofsimple quadratic forms and perform new RMT convergences of structured quadraticforms and SINR loss in the large dimensional regime, i.e. the size and thenumber of the data tend to infinity at the same rate. We show throughsimulations the interest of our approach compared to the previous works whenthe restrictive hypothesis is no longer verified.

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