Large region acoustic source mapping: A generalized sparse constrained deconvolution approach
Shengkui Zhao, Cagdas Tuna, Thi Ngoc Tho Nguyen, Douglas L. Jones
Abstract
This paper presents a generalized multiple-point sparse constrained deconvolution approach for mapping acoustic noise sources in large regions using a movable array. Extended from our previous MPSC-DAMAS approach, we first derive a generalized inverse problem relating to the source powers and the array manifold using a generic beamformer and an explicit measurement noise model. We then propose a generalized MPSC-DAMAS (GMPSC-DAMAS) approach for resolving the inverse problem. A new parameter setting method based on a multiple-point minimum-variance-distortionless-response (MVDR) beamformer is also presented. The realizations of the GMPSC-DAMAS approach using the delay- and-sum (DAS) beamformer and the MVDR beamformer are evaluated. Simulation results show the proposed GMPSC-DAMAS approach achieves much lower absolute power estimation errors and processing time than the MPSC-DAMAS approach in terms of number of sources and robustness to measurement noise.
BibTeX
@inproceedings{icassp2016_largeregionacous,
title = {Large region acoustic source mapping: A generalized sparse constrained deconvolution approach},
author = {Shengkui Zhao and Cagdas Tuna and Thi Ngoc Tho Nguyen and Douglas L. Jones},
booktitle = {ICASSP 2016},
year = {2016}
}