Efficient convex optimization for energy-based acoustic sensor self-localization and source localization in sensor networks

The energy reading has been an efficient and attractive measure for collaborative acoustic source localization in practical application due to its cost saving in both energy and computation capability. The maximum likelihood problems by fusing received acoustic energy readings transmitted from local...

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Main Authors: Li, Shuangquan, Yan, Yongsheng, Wang, Haiyan, Shen, Xiaohong, Leng, Bing
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2018
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Online Access:https://hdl.handle.net/10356/87509
http://hdl.handle.net/10220/45408
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-875092020-03-07T13:57:30Z Efficient convex optimization for energy-based acoustic sensor self-localization and source localization in sensor networks Li, Shuangquan Yan, Yongsheng Wang, Haiyan Shen, Xiaohong Leng, Bing School of Electrical and Electronic Engineering Centre for Infocomm Technology (INFINITUS) Source Localization Sensor Self-localization The energy reading has been an efficient and attractive measure for collaborative acoustic source localization in practical application due to its cost saving in both energy and computation capability. The maximum likelihood problems by fusing received acoustic energy readings transmitted from local sensors are derived. Aiming to efficiently solve the nonconvex objective of the optimization problem, we present an approximate estimator of the original problem. Then, a direct norm relaxation and semidefinite relaxation, respectively, are utilized to derive the second-order cone programming, semidefinite programming or mixture of them for both cases of sensor self-location and source localization. Furthermore, by taking the colored energy reading noise into account, several minimax optimization problems are formulated, which are also relaxed via the direct norm relaxation and semidefinite relaxation respectively into convex optimization problems. Performance comparison with the existing acoustic energy-based source localization methods is given, where the results show the validity of our proposed methods. Published version 2018-07-31T04:54:42Z 2019-12-06T16:43:25Z 2018-07-31T04:54:42Z 2019-12-06T16:43:25Z 2018 Journal Article Yan, Y., Wang, H., Shen, X., Leng, B., & Li, S. (2018). Efficient convex optimization for energy-based acoustic sensor self-localization and source localization in sensor networks. Sensors, 18(5), 1646-. 1424-8220 https://hdl.handle.net/10356/87509 http://hdl.handle.net/10220/45408 10.3390/s18051646 en Sensors © 2018 by The Author(s). Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Source Localization
Sensor Self-localization
spellingShingle Source Localization
Sensor Self-localization
Li, Shuangquan
Yan, Yongsheng
Wang, Haiyan
Shen, Xiaohong
Leng, Bing
Efficient convex optimization for energy-based acoustic sensor self-localization and source localization in sensor networks
description The energy reading has been an efficient and attractive measure for collaborative acoustic source localization in practical application due to its cost saving in both energy and computation capability. The maximum likelihood problems by fusing received acoustic energy readings transmitted from local sensors are derived. Aiming to efficiently solve the nonconvex objective of the optimization problem, we present an approximate estimator of the original problem. Then, a direct norm relaxation and semidefinite relaxation, respectively, are utilized to derive the second-order cone programming, semidefinite programming or mixture of them for both cases of sensor self-location and source localization. Furthermore, by taking the colored energy reading noise into account, several minimax optimization problems are formulated, which are also relaxed via the direct norm relaxation and semidefinite relaxation respectively into convex optimization problems. Performance comparison with the existing acoustic energy-based source localization methods is given, where the results show the validity of our proposed methods.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Li, Shuangquan
Yan, Yongsheng
Wang, Haiyan
Shen, Xiaohong
Leng, Bing
format Article
author Li, Shuangquan
Yan, Yongsheng
Wang, Haiyan
Shen, Xiaohong
Leng, Bing
author_sort Li, Shuangquan
title Efficient convex optimization for energy-based acoustic sensor self-localization and source localization in sensor networks
title_short Efficient convex optimization for energy-based acoustic sensor self-localization and source localization in sensor networks
title_full Efficient convex optimization for energy-based acoustic sensor self-localization and source localization in sensor networks
title_fullStr Efficient convex optimization for energy-based acoustic sensor self-localization and source localization in sensor networks
title_full_unstemmed Efficient convex optimization for energy-based acoustic sensor self-localization and source localization in sensor networks
title_sort efficient convex optimization for energy-based acoustic sensor self-localization and source localization in sensor networks
publishDate 2018
url https://hdl.handle.net/10356/87509
http://hdl.handle.net/10220/45408
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