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Sensors 2018, 18(1), 8; doi:10.3390/s18010008

A Support Vector Learning-Based Particle Filter Scheme for Target Localization in Communication-Constrained Underwater Acoustic Sensor Networks

1,†,* , 1,†
,
1
,
1
and
1,2
1
Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
2
School of Electronic Information and Electrical Engineering, Shanghai Jiaotong University, Shanghai 200030, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 6 November 2017 / Revised: 15 December 2017 / Accepted: 15 December 2017 / Published: 21 December 2017
(This article belongs to the Special Issue Sensor Signal and Information Processing)
View Full-Text   |   Download PDF [451 KB, uploaded 21 December 2017]   |  

Abstract

Target localization, which aims to estimate the location of an unknown target, is one of the key issues in applications of underwater acoustic sensor networks (UASNs). However, the constrained property of an underwater environment, such as restricted communication capacity of sensor nodes and sensing noises, makes target localization a challenging problem. This paper relies on fractional sensor nodes to formulate a support vector learning-based particle filter algorithm for the localization problem in communication-constrained underwater acoustic sensor networks. A node-selection strategy is exploited to pick fractional sensor nodes with short-distance pattern to participate in the sensing process at each time frame. Subsequently, we propose a least-square support vector regression (LSSVR)-based observation function, through which an iterative regression strategy is used to deal with the distorted data caused by sensing noises, to improve the observation accuracy. At the same time, we integrate the observation to formulate the likelihood function, which effectively update the weights of particles. Thus, the particle effectiveness is enhanced to avoid “particle degeneracy” problem and improve localization accuracy. In order to validate the performance of the proposed localization algorithm, two different noise scenarios are investigated. The simulation results show that the proposed localization algorithm can efficiently improve the localization accuracy. In addition, the node-selection strategy can effectively select the subset of sensor nodes to improve the communication efficiency of the sensor network. View Full-Text
Keywords: target; localization; particle filter; support vector learning; underwater acoustic sensor networks (UASNs) target; localization; particle filter; support vector learning; underwater acoustic sensor networks (UASNs)
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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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MDPI and ACS Style

Li, X.; Zhang, C.; Yan, L.; Han, S.; Guan, X. A Support Vector Learning-Based Particle Filter Scheme for Target Localization in Communication-Constrained Underwater Acoustic Sensor Networks. Sensors 2018, 18, 8.

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