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 distributedan average consensus (AC) is used to avoid the need for a fusion center. The algorithms are based on a recently reported finitetime 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.

DOA estimation in a distributed optimization framework: a sparse approach based on consensus ADMM implementation

“…For the distributed adaptive estimation of specific node signals 15 , introduced part of the prior knowledge of the target source steering matrix, and studied signal enhancement or noise reduction of specific nodes based on multi-channel Wiener filter. On the basis of Scaglione et al 14 , chen et al 16 divided uniform linear array (ULA) array into multiple subarrays, and each subarray was connected to its own processor. Among each processor, distributed power method 14 and finite-time average consensus (AC) technique 17 were used to realize the distributed DOA estimation of several classical algorithms.…”