Version 1
: Received: 18 September 2017 / Approved: 18 September 2017 / Online: 18 September 2017 (17:04:13 CEST)
How to cite:
Komari Alaie, H.; Farsi, H. Passive Sonar Target Detection Using Statistical Classifier and Adaptive Threshold. Preprints2017, 2017090084. https://doi.org/10.20944/preprints201709.0084.v1.
Komari Alaie, H.; Farsi, H. Passive Sonar Target Detection Using Statistical Classifier and Adaptive Threshold. Preprints 2017, 2017090084. https://doi.org/10.20944/preprints201709.0084.v1.
Cite as:
Komari Alaie, H.; Farsi, H. Passive Sonar Target Detection Using Statistical Classifier and Adaptive Threshold. Preprints2017, 2017090084. https://doi.org/10.20944/preprints201709.0084.v1.
Komari Alaie, H.; Farsi, H. Passive Sonar Target Detection Using Statistical Classifier and Adaptive Threshold. Preprints 2017, 2017090084. https://doi.org/10.20944/preprints201709.0084.v1.
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) which increase the detection error in shallow water. 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 prior distribution is estimated by Maximum Likelihood algorithm. Finally, target was detected by combining the detection points in both domains using LMS adaptive filter. Results of this paper has showed that proposed method has improved true detection rate about 27% compare other the best detection method.
Copyright:
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.
