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A distributed particle filtering approach for multiple acoustic source tracking using an acoustic vector sensor network

Different centralized approaches such as least-squares (LS) and particle filtering (PF) algorithms have been developed to localize an acoustic source by using a distributed acoustic vector sensor (AVS) array. However, such algorithms are either not applicable for multiple sources or rely heavily on...

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Main Authors: Zhong, X., Mohammadi, A., Premkumar, A.B., Asif, A.
Format: Article
Language:English
Published: Elsevier Science Bv, Po Box 211, 1000 Ae Amsterdam, Netherlands 2015
Subjects:
T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.um.edu.my/13822/1/A_distributed_particle_filtering_approach_for_multiple_acoustic.pdf
http://eprints.um.edu.my/13822/
http://www.sciencedirect.com/science/article/pii/S016516841400454X
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spelling my.um.eprints.138222015-07-29T03:16:16Z http://eprints.um.edu.my/13822/ A distributed particle filtering approach for multiple acoustic source tracking using an acoustic vector sensor network Zhong, X. Mohammadi, A. Premkumar, A.B. Asif, A. T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Different centralized approaches such as least-squares (LS) and particle filtering (PF) algorithms have been developed to localize an acoustic source by using a distributed acoustic vector sensor (AVS) array. However, such algorithms are either not applicable for multiple sources or rely heavily on sensor-processor communication. In this paper, a distributed unscented PF (DUPF) approach is proposed for multiple acoustic source tracking. At each distributed AVS node, the first-order and the second-order statistics of the local state are estimated by using an unscented information filter (UIF) based PF. The UIF is employed to approximate the optimum importance function due to its simplicity, by which the matrix operation is the state information matrix rather than the covariance matrix of the measurement sequence. These local statistics are then fused between neighbor nodes and a consensus filter is applied to achieve a global estimation. In such an architecture, only the state statistics need to be transmitted among the neighbor nodes. Consequently, the communication cost can be reduced. The distributed posterior Cramer-Rao bound is also derived. Simulation results show that the performance of the DUPF tracking approach is similar to that of centralized PF algorithm and significantly better than that of LS algorithms. (C) 2014 Elsevier B.V. All rights reserved. Elsevier Science Bv, Po Box 211, 1000 Ae Amsterdam, Netherlands 2015-03 Article PeerReviewed application/pdf en http://eprints.um.edu.my/13822/1/A_distributed_particle_filtering_approach_for_multiple_acoustic.pdf Zhong, X. and Mohammadi, A. and Premkumar, A.B. and Asif, A. (2015) A distributed particle filtering approach for multiple acoustic source tracking using an acoustic vector sensor network. Signal Processing, 108. pp. 589-603. ISSN 0165-1684 http://www.sciencedirect.com/science/article/pii/S016516841400454X DOI 10.1016/j.sigpro.2014.09.031
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
language English
topic T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
Zhong, X.
Mohammadi, A.
Premkumar, A.B.
Asif, A.
A distributed particle filtering approach for multiple acoustic source tracking using an acoustic vector sensor network
description Different centralized approaches such as least-squares (LS) and particle filtering (PF) algorithms have been developed to localize an acoustic source by using a distributed acoustic vector sensor (AVS) array. However, such algorithms are either not applicable for multiple sources or rely heavily on sensor-processor communication. In this paper, a distributed unscented PF (DUPF) approach is proposed for multiple acoustic source tracking. At each distributed AVS node, the first-order and the second-order statistics of the local state are estimated by using an unscented information filter (UIF) based PF. The UIF is employed to approximate the optimum importance function due to its simplicity, by which the matrix operation is the state information matrix rather than the covariance matrix of the measurement sequence. These local statistics are then fused between neighbor nodes and a consensus filter is applied to achieve a global estimation. In such an architecture, only the state statistics need to be transmitted among the neighbor nodes. Consequently, the communication cost can be reduced. The distributed posterior Cramer-Rao bound is also derived. Simulation results show that the performance of the DUPF tracking approach is similar to that of centralized PF algorithm and significantly better than that of LS algorithms. (C) 2014 Elsevier B.V. All rights reserved.
format Article
author Zhong, X.
Mohammadi, A.
Premkumar, A.B.
Asif, A.
author_facet Zhong, X.
Mohammadi, A.
Premkumar, A.B.
Asif, A.
author_sort Zhong, X.
title A distributed particle filtering approach for multiple acoustic source tracking using an acoustic vector sensor network
title_short A distributed particle filtering approach for multiple acoustic source tracking using an acoustic vector sensor network
title_full A distributed particle filtering approach for multiple acoustic source tracking using an acoustic vector sensor network
title_fullStr A distributed particle filtering approach for multiple acoustic source tracking using an acoustic vector sensor network
title_full_unstemmed A distributed particle filtering approach for multiple acoustic source tracking using an acoustic vector sensor network
title_sort distributed particle filtering approach for multiple acoustic source tracking using an acoustic vector sensor network
publisher Elsevier Science Bv, Po Box 211, 1000 Ae Amsterdam, Netherlands
publishDate 2015
url http://eprints.um.edu.my/13822/1/A_distributed_particle_filtering_approach_for_multiple_acoustic.pdf
http://eprints.um.edu.my/13822/
http://www.sciencedirect.com/science/article/pii/S016516841400454X
_version_ 1643689658326974464
score 13.211869

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