151. The Underwater Acoustic Target Recognition Algorithm Based on Evidence Clustering
- Author
-
Jianhua Yang, Yang Zhang, and Hong Hou
- Subjects
computational efficiency ,business.industry ,Computer science ,pattern recognition ,General Engineering ,020206 networking & telecommunications ,Pattern recognition ,TL1-4050 ,02 engineering and technology ,Object (computer science) ,Class (biology) ,support vector machines ,k-nearest neighbors algorithm ,Support vector machine ,clustering algorithm ,evidence k-nearest neighbor ,Classification rule ,Pattern recognition (psychology) ,0202 electrical engineering, electronic engineering, information engineering ,Feature (machine learning) ,020201 artificial intelligence & image processing ,Artificial intelligence ,Cluster analysis ,business ,Motor vehicles. Aeronautics. Astronautics - Abstract
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.
- Published
- 2018