Distributed Algorithms for Array Signal Processing

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 d...

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Veröffentlicht in:	IEEE transactions on signal processing Jg. 69; S. 4607 - 4622
Hauptverfasser:	Chen, Po-Chih, Vaidyanathan, Palghat
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Array signal processing Arrays Capon beamforming Convergence convolutional beamspace Direction-of-arrival estimation Distributed algorithms Distributed or decentralized DOA estimation ESPRIT Estimation Exact solutions FOCUSS Principal components analysis root-MUSIC Signal processing Signal processing algorithms Smoothing methods Spatial smoothing
ISSN:	1053-587X, 1941-0476
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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.
AbstractList	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.
Author	Chen, Po-Chih Vaidyanathan, Palghat
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SubjectTerms	Algorithms Array signal processing Arrays Capon beamforming Convergence convolutional beamspace Direction-of-arrival estimation Distributed algorithms Distributed or decentralized DOA estimation ESPRIT Estimation Exact solutions FOCUSS Principal components analysis root-MUSIC Signal processing Signal processing algorithms Smoothing methods Spatial smoothing
Title	Distributed Algorithms for Array Signal Processing
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