The Application Of Support Vector Machines In Underwater Target Recognition

The work of the paper derived from some subject "XXXX basic research" , which mainly use the underwater target feature extraction and recognition and support vector machine-based method to identify chitinous object and stone targets, in order to increase the recognition rate of underwater targets.The methods in this paper that is to make identification by echo characteristics of the underwater target. Specifically, the active sonar give a signal to identify the underwater targets, and the valid characteristics information of targets were extracted from echoes, and then the characteristics information were sent to the classifier to identify and categories.This paper mainly discussed as follows:1. Overview the context and meaning of the underwater target recognition, as well as domestic and abroad detection methods ,and research. status on the underwater targets.2. Feature extraction process is the collection of the target echo of the signal characteristics of the chitinous object and stone , transforming to a different space, extracted a sample to reflect the essential character of the feature vector, and send feature vector in to the classifier. In order to study the different methods of extracting characteristics, using four feature extraction methods, that is, the wavelet packet energy feature extraction, filtering constant Q sub-band energy feature extraction, feature extraction power Bispectrum principal, component analysis and feature extraction. Then the goal of the chitinous object and stone extraction by the characteristics of effective use of K-L transform, respectively, in order to determine the four feature extraction methods are effective.3. This article describe the statistical theory and support vector machines in detail. The support vector machine based on statistical theory have theoretical foundation stability of mathematics and rigorous theoretical analysis, the theory has a complete, global optimization, adaptability, and capacity of promote, the machine learning is a new approach and new hot spots. It uses the principle by which the structure risk will be minimization, associate with statistical study, machine learning and neural networks, to minimize the risk of experience, which has effectively improved the ability of generalization algorithm. The kernel arithmetic successful use make the low points which can not be mapped to the high-dimensional space-dimensional space can be divided into, and effectively eliminates the shortcomings of the dimensions.4. Three SVM algorithms are set forth, such as the SMO algorithm, and three categories classifier are designed5. Using four methods to extract feature of the measure of pensile goal's data, burial site measurement data and buried scan measurement data separately, and then send the results to three categories classifier to contrast identifying and classifying performance of the classifier.Now, the application of SVM algorithms is still consummating unceasingly, and SVM algorithms will be applied in broader area. It is of vital significance of underwater target identification that the application of SVM and advantage of its performance in this article.