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http://dx.doi.org/10.5391/JKIIS.2013.23.5.418

Two-Dimensional Localization Problem under non-Gaussian Noise in Underwater Acoustic Sensor Networks  

Lee, DaeHee (School of Electronic Engineering, Kumoh National Institute of Technology)
Yang, Yeon-Mo (School of Electronic Engineering, Kumoh National Institute of Technology)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.23, no.5, 2013 , pp. 418-422 More about this Journal
Abstract
This paper has considered the location estimation problem in two dimension space by using a non-linear filter under non-Gaussian noise in underwater acoustic sensor networks(UASNs). Recently, the extended Kalman filter (EKF) is widely used in location estimation. However, the EKF has a lot of problems in the non-linear system under the non-gaussian noise environment like underwater environment. In this paper, we propose the improved Two-Dimension Particle Filter (TDPF) using the re-interpretation distribution techniques based on the maximum likelihood (ML). Through the simulation, we compared and analyzed the proposed TDPF with the EKF under the non-Gaussian underwater sensor networks. Finally, we determined that the TDPF's result shows more accurate localization than EKF's result.
Keywords
Location Estimation; Non-Gaussian noise; Particle filter; Extended Kalman filter; Underwater Acoustic Sensor Networks;
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Times Cited By KSCI : 4  (Citation Analysis)
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