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The Underwater Acoustic Target Recognition Algorithm Based on Evidence Clustering
- Source :
- Xibei Gongye Daxue Xuebao, Vol 36, Iss 1, Pp 96-102 (2018)
- Publication Year :
- 2018
- Publisher :
- The Northwestern Polytechnical University, 2018.
-
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.
- 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
Subjects
Details
- Language :
- Chinese
- ISSN :
- 10002758
- Volume :
- 36
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Xibei Gongye Daxue Xuebao
- Accession number :
- edsair.doi.dedup.....23b88499911d912d22c7c6cd4601f3d2