An Automated Approach to Passive Sonar Classification Using Binary Image Features
An Automated Approach to Passive Sonar Classification Using Binary Image Features
摘要
This paper proposes a new method for ship recognition and classification using sound produced and radiated underwater. To do so, a three-step procedure is proposed. First, the preprocessing operations are utilized to reduce noise effects and provide signal for feature extraction. Second, a binary image, made from frequency spectrum of signal segmentation, is formed to extract effective features. Third, a neural classifier is designed to classify the signals. Two approaches, the proposed method and the fractal-based method are compared and tested on real data. The comparative results indicated better recognition ability and more robust performance of the proposed method than the fractal-based method. Therefore, the proposed method could improve the recognition accuracy of underwater acoustic targets.
参考文献30
-
1Bao F, Li C, Wang X, Wang Q, Du S (2010). Ship classification using nonlinear features of radiated sound: An approach based on empirical mode decomposition. The Journal of the Acoustical Society of America, 128(1), 206-214. DOI: http://dx.doi.org/l 0.1121/1.3436543. 被引量：1
-
2Becchetti C, Ricotti LP (1999). Speech recognition. John Wiley, New York, 1-67. 被引量：1
-
3Chen C, Lee J, Lin M (2000). Classification of trader-water signals using neural network. Tamkang Journal of Science and Engineering, 3(1), 31-48. 被引量：1
-
4Diamant R, Lampe L (2013). Underwater localization with time synchronization and propagation speed uncertainties. IEEE Transactions on Mobile Computing, 12(7), 1257-1269. DOI: 10.1109/TMC.2012.100. 被引量：1
-
5Duda RO, Hart PE, Stork DG (2000). Pattern classification. John Wiley, New York, 282-320. 被引量：1
-
6Eiekstedt D, Schmidt H (2003). A low-frequency sonar for sensor adaptive, multistatic, detection and classification of underwater targets with AUVs. Proceedings of the OCEANS, San Diego, CA, USA. 1440-1447. DOI: 10.1109/OCEANS.2003.178074. 被引量：1
-
7Farrokhrooz M, Karimi M (2005) probabilistic neural network Ship noise classification using and AR model coefficients. Proceedings of the OCEANS, Washington, DC, USA, 1107-1110. DOI: 10.1109/OCEANSE.2005.1513213. 被引量：1
-
8Farrokhrooz M, Karimi M (2011). Marine vessels acoustic radiated noise classification in passive sonar using probabilistic neural network and spectral features. Intelligent Automation and Soft Computing, 17(3), 369-383. DOI: http://dx.doi.org/10.1080/10798587.2011.10643155. 被引量：1
-
9He Xiying, Cheng Jinfang, He Guangjin (2010). Application of BP neural network and higher order spectrum for ship-radiated noise classification. Proceedings of the 2nd International Conference on Future Computer and Communication, Wuhan, China, 712-716. DOI: 10.1109/ICFCC.2010.5497336. 被引量：1
-
10Howell B, Wood S (2003). Passive sonar recognition and analysis using hybrid neural networks. Proceedings of the OCEANS, San Diego, USA, 1917-1924. DOI: 10.1109/OCEANS.2003.178182. 被引量：1
-
1王学志,吴克桐,凃英,蔡惠智.基于千兆以太网信道的被动声纳系统的设计与实现[J].微计算机应用,2010,31(7):51-57.
-
2让无线打印触手可及——惠普LaserJet 1000系列[J].电脑时空,2005(6):33-33.
-
3张振华,笪良龙,谢骏.一种新的被动声纳作用距离估计方法[J].舰船电子工程,2010,30(10):170-172.
-
4WU Guoqing,JI Shunxin,LI Jing ,CHEN Yaoming(Institute of Acoustics, Chinese Academy Of Sciences Beijing 100080)(National Laboratory of Pattern Recognition, Chinese Academy of Sciences Beijing 100080)LI Xungao(Naval Submarine Institute Qindao 266071).Passive sonar identification Ⅰ:Overall framework and extraction of line-spectrum[J].Chinese Journal of Acoustics,1999,18(2):128-135.
-
5付德安.浅析送电线路初步设计[J].大科技,2012(14):66-67.
-
6陈斌.浅谈送电线路初步设计[J].中国科技博览,2011(27):130-130.
-
7袁月芳.桥梁加固设计应用[J].城市道桥与防洪,2012(4):108-111. 被引量：1
-
8于东敏,史建政.基于物联网的高速铁轨控制故障监测方法研究[J].计算机仿真,2012,29(9):379-382. 被引量：1
-
9李伟,张建秋,张砚,汪源源,胡波,李旦.提高光声成像质量的Renyi熵方法[J].生物医学工程学进展,2012,33(1):1-5. 被引量：2
-
10汪成亮,汪连伟.基于特征的江面轮船识别算法[J].计算机应用研究,2011,28(6):2352-2354. 被引量：4
