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Appl. Sci. 2018, 8(1), 61; doi:10.3390/app8010061

Passive Sonar Target Detection Using Statistical Classifier and Adaptive Threshold

Department of Electrical and Computer Engineering, University of Birjand, Birjand 97175, Iran
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Received: 16 September 2017 / Revised: 24 December 2017 / Accepted: 27 December 2017 / Published: 3 January 2018
(This article belongs to the Section Computer Science and Electrical Engineering)

Abstract

This paper presents the results of an experimental investigation about target detecting with passive sonar in Persian Gulf. Detecting propagated sounds in the water is one of the basic challenges of the researchers in sonar field. This challenge will be complex in shallow water (like Persian Gulf) and noise less vessels. Generally, in passive sonar, the targets are detected by sonar equation (with constant threshold) that increases the detection error in shallow water. The purpose of this study is proposed a new method for detecting targets in passive sonars using adaptive threshold. In this method, target signal (sound) is processed in time and frequency domain. For classifying, Bayesian classification is used and posterior distribution is estimated by Maximum Likelihood Estimation algorithm. Finally, target was detected by combining the detection points in both domains using Least Mean Square (LMS) adaptive filter. Results of this paper has showed that the proposed method has improved true detection rate by about 24% when compared other the best detection method. View Full-Text
Keywords: passive sonar; target detection; adaptive threshold; Bayesian classifier; k-means; maximum likelihood estimation passive sonar; target detection; adaptive threshold; Bayesian classifier; k-means; maximum likelihood estimation
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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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Komari Alaie, H.; Farsi, H. Passive Sonar Target Detection Using Statistical Classifier and Adaptive Threshold. Appl. Sci. 2018, 8, 61.

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