Acoustic imaging of sparse Sources with Orthogonal Matching Pursuit and clustering of basis vectors

Trond F. Bergh; Ines Hafizovic; Sverre Holm

ICASSP 2017accepted0 citations

Acoustic imaging of sparse Sources with Orthogonal Matching Pursuit and clustering of basis vectors

Trond F. Bergh, Ines Hafizovic, Sverre Holm

Abstract

We have devised a greedy method for finding solutions to the sparse Deconvolution Approach for the Mapping of Acoustic Sources inverse problem using a variant of Orthogonal Matching Pursuit. The algorithm has two stages, wherein the first stage consists of selecting a subset of the basis vectors iteratively via a regularized inverse of the point spread function, and the second stage consists of constructing point source solutions using this basis subset and its coefficients via hierarchical agglomerative clustering. We have evaluated the algorithm on both synthetic and real data, and show that the overall accuracy in terms of direction of arrival and reconstructed source power is better than four other state of the art methods.

BibTeX

@inproceedings{icassp2017_acousticimagingo,
  title = {Acoustic imaging of sparse Sources with Orthogonal Matching Pursuit and clustering of basis vectors},
  author = {Trond F. Bergh and Ines Hafizovic and Sverre Holm},
  booktitle = {ICASSP 2017},
  year = {2017}
}