Wiener Filter Approximations Without Covariance Matrix Inversion

Damale, Pranav U.; Chong, Edwin K. P.; Scharf, Louis L.

doi:10.1109/ojsp.2023.3292714

Cited by 2 publications

(1 citation statement)

References 39 publications

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“…However, the resulting Wiener filter based on TOT decomposition solves the involved sets of Wiener-Hopf equations using the conventional approach, which relies on matrix inversion. Alternatively, different iterative techniques could be used to avoid such an operation [29][30][31], like the conjugate gradient (CG) method [32]. In [33], the CG algorithm has been applied in conjunction with the NKP-based technique from [8], showing improved performance.…”

Section: Introductionmentioning

confidence: 99%

Wiener Filter Using the Conjugate Gradient Method and a Third-Order Tensor Decomposition

Benesty,

Paleologu,

Stanciu

et al. 2024

Applied Sciences

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In linear system identification problems, the Wiener filter represents a popular tool and stands as an important benchmark. Nevertheless, it faces significant challenges when identifying long-length impulse responses. In order to address the related shortcomings, the solution presented in this paper is based on a third-order tensor decomposition technique, while the resulting sets of Wiener–Hopf equations are solved with the conjugate gradient (CG) method. Due to the decomposition-based approach, the number of coefficients (i.e., the parameter space of the filter) is greatly reduced, which results in operating with smaller data structures within the algorithm. As a result, improved robustness and accuracy can be achieved, especially in harsh scenarios (e.g., limited/incomplete sets of data and/or noisy conditions). Besides, the CG-based solution avoids matrix inversion operations, together with the related numerical and complexity issues. The simulation results are obtained in a network echo cancellation scenario and support the performance gain. In this context, the proposed iterative Wiener filter outperforms the conventional benchmark and also some previously developed counterparts that use matrix inversion or second-order tensor decompositions.

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Section: Introductionmentioning

confidence: 99%

Wiener Filter Using the Conjugate Gradient Method and a Third-Order Tensor Decomposition

Benesty,

Paleologu,

Stanciu

et al. 2024

Applied Sciences

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A Fourth-Order Tensorial Wiener Filter Using the Conjugate Gradient Method

Dogariu,

Costea,

Paleologu

et al. 2024

Symmetry

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The recently developed iterative Wiener filter using a fourth-order tensorial (FOT) decomposition owns appealing performance in the identification of long length impulse responses. It relies on the nearest Kronecker product representation (with particular intrinsic symmetry features), together with low-rank approximations. Nevertheless, this new iterative filter requires matrix inversion operations when solving the Wiener–Hopf equations associated with the component filters. In this communication, we propose a computationally efficient version that relies on the conjugate gradient (CG) method for solving these sets of equations. The proposed solution involves a specific initialization of the component filters and sequential connections between the CG cycles. Different FOT-based decomposition setups are also analyzed from the point of view of the resulting parameter space. Experimental results obtained in the context of echo cancellation confirm the good behavior of the proposed approach and its superiority in comparison to the conventional Wiener filter and other decomposition-based versions.

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Wiener Filter Approximations Without Covariance Matrix Inversion

Cited by 2 publications

References 39 publications

Wiener Filter Using the Conjugate Gradient Method and a Third-Order Tensor Decomposition

Wiener Filter Using the Conjugate Gradient Method and a Third-Order Tensor Decomposition

A Fourth-Order Tensorial Wiener Filter Using the Conjugate Gradient Method

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