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Appl. Sci. 2018, 8(2), 225; doi:10.3390/app8020225

Target Localization in Underwater Acoustic Sensor Networks Using RSS Measurements

1
Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China
2
Xiamen Key Laboratory of Mobile Multimedia Communications, Huaqiao University, 668 Jimei Avenue, Xiamen 361021, China
*
Author to whom correspondence should be addressed.
Received: 28 November 2017 / Revised: 26 January 2018 / Accepted: 29 January 2018 / Published: 1 February 2018
(This article belongs to the Special Issue Underwater Acoustics, Communications and Information Processing)
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Abstract

This paper addresses the target localization problems based on received signal strength (RSS) measurements in underwater acoustic wireless sensor network (UWSN). Firstly, the problems based on the maximum likelihood (ML) criterion for estimating target localization in cases of both known and unknown transmit power are respectively derived, and fast implementation algorithms are proposed by transforming the non-convex problems into a generalized trust region subproblem (GTRS) frameworks. A three-step procedure is also provided to enhance the estimation accuracy in the unknown target transmit power case. Furthermore, the Cramer–Rao lower bounds (CRLBs) in both cases are derived. Computer simulation results show the superior performance of the proposed methods in the underwater environment. View Full-Text
Keywords: underwater acoustic wireless sensor network (UWSN); target localization; received signal strength (RSS); Cramer–Rao lower bounds (CRLBs) underwater acoustic wireless sensor network (UWSN); target localization; received signal strength (RSS); Cramer–Rao lower bounds (CRLBs)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Chang, S.; Li, Y.; He, Y.; Wang, H. Target Localization in Underwater Acoustic Sensor Networks Using RSS Measurements. Appl. Sci. 2018, 8, 225.

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