Please use this identifier to cite or link to this item:
Title: Efficient convex optimization for energy-based acoustic sensor self-localization and source localization in sensor networks
Authors: Li, Shuangquan
Yan, Yongsheng
Wang, Haiyan
Shen, Xiaohong
Leng, Bing
Keywords: Source Localization
Sensor Self-localization
Issue Date: 2018
Source: 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-.
Series/Report no.: Sensors
Abstract: 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.
ISSN: 1424-8220
Rights: © 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 (
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

Google ScholarTM



Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.