Performance of subspace-based algorithms associated with the sample sign covariance matrix

Abeida, Habti; Delmas, Jean-Pierre

doi:10.1016/j.dsp.2022.103767

Search citation statements

Order By: Relevance

Paper Sections

Select...

Estimators2

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Other1

Book1

Relationship

Self Cite1

Independent1

Authors

Journals

Cited by 2 publications

(2 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then plugging the expressions ( 11), ( 16), (18) of R rx and ( 13), (17), (19) of R rx into ( 27) and (28), and using Π x S x = 0 and Π xS x = 0, the following result which extends [34, Th. IV.1] and [30,Th. 3]:…”

Section: Estimatorsmentioning

confidence: 99%

See 1 more Smart Citation

Unified asymptotic distribution of subspace projectors in complex elliptically symmetric models

Delmas,

Abeida

2023

2023 IEEE Statistical Signal Processing Workshop (SSP)

Self Cite

View full text Add to dashboard Cite

The statistical performance of subspace-based algorithms depends on the deterministic and stochastic statistical model of the noisy linear mixture of the data, the estimate of the projector, and the algorithm that estimates the parameters from the projector. This paper presents different circular and non-circular complex elliptically symmetric (CES) models of the data and different associated non-robust and robust covariance estimators whose asymptotic distributions are derived. This allows us to unify and complement the asymptotic distribution of subspace projectors adapted to these models and to prove several invariance properties that have impacts on the parameters to be estimated in CES data models.

show abstract

Section: Estimatorsmentioning

confidence: 99%

“…Result 1: The covariance matrices R πx and R πx of the asymptotic distribution (25) and (26) of the different projector estimators Π have an unified structure given by (30) where Π(θ) are the projection matrices…”

Section: Estimatorsmentioning

confidence: 99%