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Published August 2, 2021 | Accepted Version
Journal Article Open

Distributed Algorithms for Array Signal Processing

Abstract

Distributed or decentralized estimation of covariance, and distributed principal component analysis have been introduced and studied in the signal processing community in recent years, and applications in array processing have been indicated in some detail. Inspired by these, this paper provides a detailed development of several distributed algorithms for array processing. New distributed algorithms are proposed for DOA estimation methods like root-MUSIC, total least squares-ESPRIT, and FOCUSS. Other contributions include distributed design of the Capon beamformer from data, and distributed implementation of the spatial smoothing method for coherent sources. A distributed implementation of a recently proposed beamspace method called the convolutional beamspace (CBS) is also proposed. The proposed algorithms are fully distributed – an average consensus (AC) is used to avoid the need for a fusion center. The algorithms are based on a recently reported finite-time version of AC which converges to the exact solution in a finite number of iterations. Numerical examples are given throughout the paper to show the effectiveness of the proposed algorithms.

Additional Information

© 2021 IEEE. Manuscript received February 17, 2021; revised June 7, 2021; accepted July 19, 2021. Date of publication August 2, 2021; date of current version August 20, 2021. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Alexander Bertrand. This work was supported by the Office of Naval Research under Grant N00014-21-1-2521, and the California Institute of Technology.

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Accepted Version - Distributed_algorithms_for_array_signal_processing.pdf

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Additional details

Created:
August 20, 2023
Modified:
October 23, 2023