On the Recursions of Robust COMET Algorithm for Convexly Structured

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On the Recursions of Robust COMET Algorithm for Convexly Structured On the Recursions of Robust COMET Algorithm for Convexly Structured Shape Matrix Bruno Meriaux, Chengfang Ren, Arnaud Breloy, Mohammed Nabil El Korso, P Forster, Jean-Philippe Ovarlez To cite this version: Bruno Meriaux, Chengfang Ren, Arnaud Breloy, Mohammed Nabil El Korso, P Forster, et al.. On the Recursions of Robust COMET Algorithm for Convexly Structured Shape Matrix. 27th European Signal Processing Conference (EUSIPCO 2019), Sep 2019, A Coruña, Spain. hal-02155905 HAL Id: hal-02155905 https://hal.archives-ouvertes.fr/hal-02155905 Submitted on 14 Jun 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. On the Recursions of Robust COMET Algorithm for Convexly Structured Shape Matrix B. Meriaux´ ∗, C. Ren∗, A. Breloyy, M.N. El Korsoy, P. Forsterz and J.-P. Ovarlez∗x ∗SONDRA, CentraleSupelec,´ Universite´ Paris-Saclay, F-91190 Gif-sur-Yvette, France yLEME EA 4416, Universite´ Paris-Nanterre, F-92410 Ville d’Avray, France zSATIE, Universite´ Paris-Nanterre, F-94230 Cachan, France xDEMR, ONERA, Universite´ Paris-Saclay, F-91123 Palaiseau, France Abstract—This paper addresses robust estimation of structured the common Complex Angular Elliptical (CAE) distribution. shape (normalized covariance) matrices. Shape matrices most Several robust methods have been proposed to leverage Tyler’s often own a particular structure depending on the application estimator formulation [7] in the context of structured shape of interest and taking this structure into account improves estimation accuracy. In the framework of robust estimation, we matrices [8]–[13]. A COnvexly ConstrAined (COCA) shape introduce a recursive robust shape matrix estimation technique matrix estimator has been recently proposed in [10]. Iterative based on Tyler’s M-estimate for convexly structured shape Majorization-Minimization algorithms for the computation of matrices. We prove that the proposed estimator is consistent, structured CM estimates are developped in [11] and a robust asymptotically efficient and Gaussian distributed and we notice extension of COMET, named RCOMET, has been derived in that it reaches its asymptotic regime faster as the number of recursions increases. Finally, in the particular wide spreaded case [13]. The references [8], [9], [12] considered the problem of of Hermitian persymmetric structure, we study the convergence robust shape matrix estimation with symmetric structures. of the recursions of the proposed algorithm. In this paper, we propose a Recursive version of RCOMET Index Terms—Robust shape matrix estimation, elliptical dis- (R-RCOMET) based on Tyler’s M-estimate and COMET tributions, Tyler’s M-estimator, structured estimation. criterion for convexly structured shape matrices. We conduct a theoretical analysis of the asymptotic performance of the pro- I. INTRODUCTION posed estimator. We also compare the non-asymptotic behavior Most of the adaptive signal processing algorithms require a with the RCOMET method. Finally, we analyse theoretically Covariance Matrix (CM) estimation. In addition to its Hermi- the convergence of the recursions in the particular case of the tian symmetry and positive definiteness, the CM may exhibit Hermitian persymmetric structure. a particular structure related to the application of interest. For In the following, convergence in distribution and in proba- example, a linear array that is symmetrically spaced w.r.t. the bility are respectively denoted by !L and !P . AT (respectively phase center leads to the Hermitian persymmetric structure AH and A∗) stands for the transpose (respectively conjugate of the CM [1]. Another example is the Toeplitz structure for transpose and conjugate) matrix. The vec-operator vec(A) uniform linear arrays. Taking into account this structure in stacks all columns of A into a vector. The identity matrix the estimation scheme leads to a better estimation accuracy of size m is referred to as Im. The matrix Jm denotes since it decreases the degrees of freedom in the estimation the m-dimensional antidiagonal matrix, having 1 as non- problem [2]. In the Gaussian framework, this challenge has zero element. The operator ⊗ refers to the Kronecker matrix been extensively studied. Notably, the Covariance Matching product and finally, the subscript ”e” refers to the true value. Estimation Technique (COMET) has been proposed in [3]. This paper is organized as follows. In section II, a brief re- The latter is computationally less intensive than Maximum view on CAE distribution, Tyler’s M-estimate and RCOMET Likelihood (ML) estimation and still provides asymptotically procedure is presented. Section III focuses on the proposed efficient CM estimates. However, COMET is based on the algorithm and its performance analysis. We also analyse the Sample Covariance Matrix (SCM) estimate, thus it is sensi- convergence of the recursions in the case of the Hermitian tive to outliers. In a context of robust CM estimation, the persymmetric structure. Some simulations results in Section class of circular Complex Elliptically Symmetric distributions IV illustrate the theoretical analysis. (CES) turns out to be particularly suitable to model spiky radar clutter measurements [4]–[6]. Within this framework, II. BACKGROUND AND PROBLEM SETUP a distribution free estimator of the scatter matrix is derived A. Complex Angular Elliptical Distribution in [7] and referred to as Tyler’s M-estimator. Furthermore, m the normalization of zero mean CES distributed data leads to Let x 2 C be a circular centered CES distributed random vector [5] with scatter matrix M. If it exists, the covariance The work of B. Meriaux´ is partially funded by the Direction Gen´ erale´ matrix of x is proportional to M. The normalized vector de l’Armement (D.G.A). This work is also supported by the ANR ASTRID x y = ; x 6= 0, follows a CAE distribution, denoted by referenced ANR-17-ASTR-0015. kxk y ∼ Um (M). The probability density function of the vector y Algorithm 1 R-RCOMET w.r.t. spherical measure [10] reads Require: N i.i.d. data, yn ∼ Um (Me) with N > m, any −m K ≥ 1 given p(y j M) / jMj−1 yH M−1y (1) 1: Compute Mb FP from y1;:::; yN with (2) where the matrix M is defined up to an arbitrary scale factor. 2: Initialize µb0 with (4) To avoid scaling ambiguity, M is normalized according to 3: for k = 1 to K do Tr (M) = m. We refer to M as the shape matrix of y. 4: Compute µbk from (6) 5: end for B. Tyler’s M-estimator 6: return µbK From a set of N i.i.d. CAE distributed data, yn ∼ Um (M), n = 1;:::;N with N > m, Tyler’s M-estimate is the M (µ )) and asymptotically efficient and Gaussian distributed unstructured ML-estimate of the shape matrix, given by the e [13]. Specifically solution of the following fixed-point equation [7]: p N (µ − µ ) !NL ( ; ) N H b0 e 0 CRBCAE (5) m X ynyn Mb FP = −1 , H Mb FP (2) CRB ´ N H where CAE, denoting the Cramer-Rao Bound (CRB), is n=1 yn Mb FP yn detailled in [13]. Existence and uniqueness up to a scale factor of the above Although asymptotically efficient, RCOMET algorithm re- equation solution have been studied in [14]. In the follow- quires a substantial sample support to reach its asymptotic ing, the scale ambiguity is removed by fixing in the latter regime. In this paper, we propose a recursive version of solution the same constraint as for the shape matrix, i.e. RCOMET, for which we conduct a theoretical analysis of h i Tr Mb FP = m. The solution Mb FP is obtained by an iterative its asymptotic performance; we also notice that the latter are achieved at lower sample support. Furthermore, we analyse the algorithm, Mk+1 = H (Mk) with the normalization on the convergence behavior for a particular strucuture: the Hermitian trace, which converges to Mb FP, for any initialization point [7], persymmetric one. [15]. Furthermore, Mb FP is a consistent, unbiased estimator of M and its asymptotic distribution is given by [5], [15]: III. RECURSIVE RCOMET PROCEDURE p d A. Algorithm Nvec Mb FP − M −! CN (0; Σ; ΣKm) (3) M 8 In the RCOMET objective (4), b FP plays both the role of a m + 1 T H −1 <> Σ = ΓM M ⊗ M ΓM target together with a metric specification through Mb FP . Split- with m ting these roles can lead naturally to a recursive formulation 1 H :> ΓM = Im2 − vec (M) vec (Im) where the weighting is refined at each step. For a finite and m given number of steps, K, we obtain the R-RCOMET estimate for µ, denoted by µK and achieved at the k-th iteration by where Km is the commutation matrix, which satisfies b T solving for k 2 [[1;K]] Kmvec (A) = vec A [16]. n −1o2 µk = arg min Tr Mb FP − αM (µ) M µk−1 (6) C. Problem Setup and RCOMET Algorithm b α,µ b Let us consider N i.i.d. CAE distributed observations, with µb0 given by (4) and such that Tr [M (µbK )] = m. The R-RCOMET algorithm is recapped in the box Algorithm 1. In yn ∼ Um (Me), n = 1;:::;N with N > m. The shape matrix belongs to the convex subset of Hermitian positive-definite practice, we can use a more elaborated stopping rule, e.g., a S matrices and there exists a one-to-one differentiable mapping combination of k ≤ Kmax and µbk+1 − µbk ≤ "tol kµbkk.
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