Volume 36, Number 1, February 2018
|Page(s)||96 - 102|
|Published online||18 May 2018|
The Underwater Acoustic Target Recognition Algorithm Based on Evidence Clustering
School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
2 School of Marine Engineering, Northwestern Polytechnical University, Xi'an 710072 China
In underwater acoustic target recognition, the target signal is usually complex and the samples which are difficult to obtain also have some uncertain information. In order to effectively solve these problems, the evidence clustering recognition algorithm (TECRA) is presented. In this new method, the k-nearest neighbor are first determined by using the feature distance between the object and its neighbors in each class of the training set, and a reasonable initial basic belief assignments (bba's) for each target data are constructed by the improved k-nearest neighbor classification algorithm. Then the final global bba's of the target is obtained by optimizing the objective function of the algorithm. Finally the object can be recognized by the fusion result and the classification rule presented in the paper. Several experiments based on real underwater acoustic data sets are made to test the effectiveness of TECRA in comparison with some other methods. The results indicate that TECRA can effectively improve the recognition accuracy.
Key words: clustering algorithm / computational efficiency / evidence k-nearest neighbor / support vector machines / pattern recognition
关键字 : 水声目标 / 证据聚类 / 证据K近邻 / 组合规则 / 模式识别
© 2018 Journal of Northwestern Polytechnical University. All rights reserved.
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