Underwater multi-class target classification method based on credibility estimation

Abstract

The invention provides an underwater multi-class target classification method based on credibility estimation. Firstly, an underwater multi-target data set is constructed, a power set of the underwater multi-target data set is given, then a classification result of each second-class SVM classifier is given, a contradiction factor and the confidence coefficient of each second-class SVM classifier are calculated, and therefore the classification accuracy of each underwater target class needing to be judged is obtained. The objective of the invention is to solve the problems that the confidence degree of each second-class SVM classifier cannot be determined and the results of a plurality of second-class SVM classifiers cannot be effectively fused. A Gaussian membership function is used to represent a reliability factor of each second-class SVM and a constructed confidence fusion rule is used to fuse an output result of each second-class SVM with the reliability factor. Therefore, multipletypes of underwater targets can be identified on the basis of increasing the credibility of each binary classifier, and the classification accuracy of the multiple types of underwater targets is improved.

Description

technical field [0001] The invention belongs to the field of information signal processing, and relates to theoretical methods such as underwater signal processing, support vector machines, D-S evidence theory, and classifier fusion. Background technique [0002] Since the 1980s, due to the extremely important application value of underwater target classification and recognition technology, it has become a hot spot in the field of underwater equipment research. Due to the complex and changeable marine environment and the non-stationary noise of the marine environment, the classification method of underwater multi-category targets is more difficult than the classification and recognition task of underwater two-category targets. [0003] At present, there are many methods to solve multi-classification problems, such as decision tree method, Bayesian method, artificial neural network algorithm and so on. The robustness of the decision tree method is poor, and the effectiveness...

AI Technical Summary

Problems solved by technology

Therefore, when using the multi-classification SVM algorithm, there will be two problems: one is that the weight value of each second-class SVM cannot be determined, that is, the confidence level of each second-class SVM classifier cannot be determined; Effective fusion of results from multiple binary SVM classifiers

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