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Multi-attribute overlapping radar working pattern recognition based on K-NN and SVM-BP

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Abstract

A recognition model named the SVM-NP is proposed in this paper to address the multi-attribute overlap in radar working recognition. The model is based on the K-NN boundary preselection algorithm and SVM-BP algorithm. Traditional classifiers tend to neglect the overlap of samples' attributes in classification, which leads to the low accuracy of classifiers. The K-NN boundary preselection can quickly select boundary samples from the total ones and reduce the whole samples' attribute overlap. The SVM-BP algorithm is improved based on the SVM-RFE algorithm, and the boundary samples with high attribute overlap are divided into many planes for training and testing. Compared with traditional methods, the overlap of sample attributes can be reduced twice in this model. Theoretical analysis and experimental results verify that the model proposed in this paper displays better performance in classification when appropriate parameters are provided.

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Acknowledgements

This paper is funded by the Fundamental Research Funds for the Central Universities and Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin, China. The authors are grateful to the anonymous referees for their valuable comments and suggestions that improved this paper.

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Authors and Affiliations

  1. Department of Information and Communication Engineering, Harbin Engineering University, Harbin, 150001, China

    Yanping Liao & Xinyu Chen

  2. Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin, China

    Yanping Liao

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  1. Yanping Liao
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Correspondence to Xinyu Chen.

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Liao, Y., Chen, X. Multi-attribute overlapping radar working pattern recognition based on K-NN and SVM-BP. J Supercomput 77, 9642–9657 (2021). https://doi.org/10.1007/s11227-021-03660-4

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