Robust rank constrained kronecker covariance matrix estimation

A. Breloy; Y. Sun; P. Babu; G. Ginolhac; D. P. Palomar

doi:10.1109/ACSSC.2016.7869159

Robust rank constrained kronecker covariance matrix estimation

A. Breloy, Y. Sun, P. Babu, G. Ginolhac, D. P. Palomar

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 Scopus citations

Abstract

In this paper, we consider the problem of robustly estimating a structured covariance matrix (CM). Specifically, we focus on CM structures that involve Kronecker products of low rank matrices, which often arise in the context of array processing (e.g. in MIMO-Radar, COLD array, and STAP). To tackle this problem, we derive a new Constrained Tyler's Estimators (CTE), which is defined as the minimizer of the cost function associated to Tyler's estimator under Kronecker structural constraint. Algorithms to compute these new CTEs are derived based on the Majorization-Minimization algorithmic framework.

Original language	English (US)
Title of host publication	Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
Editors	Michael B. Matthews
Publisher	IEEE Computer Society
Pages	810-814
Number of pages	5
ISBN (Electronic)	9781538639542
DOIs	https://doi.org/10.1109/ACSSC.2016.7869159
State	Published - Mar 1 2017
Event	50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 - Pacific Grove, United States Duration: Nov 6 2016 → Nov 9 2016

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)	1058-6393

Other

Other	50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
Country/Territory	United States
City	Pacific Grove
Period	11/6/16 → 11/9/16

All Science Journal Classification (ASJC) codes

Signal Processing
Computer Networks and Communications

Access to Document

10.1109/ACSSC.2016.7869159

Cite this

Breloy, A., Sun, Y., Babu, P., Ginolhac, G., & Palomar, D. P. (2017). Robust rank constrained kronecker covariance matrix estimation. In M. B. Matthews (Ed.), Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 (pp. 810-814). Article 7869159 (Conference Record - Asilomar Conference on Signals, Systems and Computers). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2016.7869159

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abstract = "In this paper, we consider the problem of robustly estimating a structured covariance matrix (CM). Specifically, we focus on CM structures that involve Kronecker products of low rank matrices, which often arise in the context of array processing (e.g. in MIMO-Radar, COLD array, and STAP). To tackle this problem, we derive a new Constrained Tyler's Estimators (CTE), which is defined as the minimizer of the cost function associated to Tyler's estimator under Kronecker structural constraint. Algorithms to compute these new CTEs are derived based on the Majorization-Minimization algorithmic framework.",

author = "A. Breloy and Y. Sun and P. Babu and G. Ginolhac and Palomar, {D. P.}",

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Breloy, A, Sun, Y, Babu, P, Ginolhac, G & Palomar, DP 2017, Robust rank constrained kronecker covariance matrix estimation. in MB Matthews (ed.), Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016., 7869159, Conference Record - Asilomar Conference on Signals, Systems and Computers, IEEE Computer Society, pp. 810-814, 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016, Pacific Grove, United States, 11/6/16. https://doi.org/10.1109/ACSSC.2016.7869159

Robust rank constrained kronecker covariance matrix estimation. / Breloy, A.; Sun, Y.; Babu, P. et al.
Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016. ed. / Michael B. Matthews. IEEE Computer Society, 2017. p. 810-814 7869159 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Robust rank constrained kronecker covariance matrix estimation

AU - Breloy, A.

AU - Sun, Y.

AU - Babu, P.

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AU - Palomar, D. P.

PY - 2017/3/1

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N2 - In this paper, we consider the problem of robustly estimating a structured covariance matrix (CM). Specifically, we focus on CM structures that involve Kronecker products of low rank matrices, which often arise in the context of array processing (e.g. in MIMO-Radar, COLD array, and STAP). To tackle this problem, we derive a new Constrained Tyler's Estimators (CTE), which is defined as the minimizer of the cost function associated to Tyler's estimator under Kronecker structural constraint. Algorithms to compute these new CTEs are derived based on the Majorization-Minimization algorithmic framework.

AB - In this paper, we consider the problem of robustly estimating a structured covariance matrix (CM). Specifically, we focus on CM structures that involve Kronecker products of low rank matrices, which often arise in the context of array processing (e.g. in MIMO-Radar, COLD array, and STAP). To tackle this problem, we derive a new Constrained Tyler's Estimators (CTE), which is defined as the minimizer of the cost function associated to Tyler's estimator under Kronecker structural constraint. Algorithms to compute these new CTEs are derived based on the Majorization-Minimization algorithmic framework.

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Breloy A, Sun Y, Babu P, Ginolhac G, Palomar DP. Robust rank constrained kronecker covariance matrix estimation. In Matthews MB, editor, Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016. IEEE Computer Society. 2017. p. 810-814. 7869159. (Conference Record - Asilomar Conference on Signals, Systems and Computers). doi: 10.1109/ACSSC.2016.7869159

Robust rank constrained kronecker covariance matrix estimation

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