Sparse Representation Classification Method for Active Sonar Target Based on Multi-order Fractional Fourier Domain Feature Fusion

Tongjing SUN; Tong LIU; Yang YANG

doi:10.11999/JEIT200634

Volume 43 Issue 3

Mar. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(3): 809-816

Tongjing SUN, Tong LIU, Yang YANG. Sparse Representation Classification Method for Active Sonar Target Based on Multi-order Fractional Fourier Domain Feature Fusion[J]. Journal of Electronics & Information Technology, 2021, 43(3): 809-816. doi: 10.11999/JEIT200634

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Tongjing SUN, Tong LIU, Yang YANG. Sparse Representation Classification Method for Active Sonar Target Based on Multi-order Fractional Fourier Domain Feature Fusion[J]. Journal of Electronics & Information Technology, 2021, 43(3): 809-816. doi: 10.11999/JEIT200634

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Sparse Representation Classification Method for Active Sonar Target Based on Multi-order Fractional Fourier Domain Feature Fusion

doi: 10.11999/JEIT200634

1.
Automation Department, Hangzhou Dianzi University, Hangzhou 310018, China
2.
School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai 201100, China

Funds: The Extension Fund from Underwater Test and Control Technology Key Laboratory (JCKYS2018207050)

Received Date: 2020-07-30
Rev Recd Date: 2021-02-08

Available Online: 2021-02-18

Publish Date: 2021-03-22

Abstract

Abstract

Marine environment noise and reverberation interference are serious, and the poor target separability is the bottleneck problem in active sonar target classification and recognition. In order to solve this problem, based on the echo signal model of active sonar target and the principle of FRactional Fourier Transform (FRFT), this paper deduces the multi-order FRFT domain feature representation form, establishes the FRFT domain sparse representation model, and proposes a method to classify the sparse representation of active sonar targets with multi-order FRFT domain feature fusion. The method achieves the purpose of suppressing noise and reverberation interference through the energy aggregation of FRFT and removing the residual of sparse decomposition; Through the fusion of multi-order FRFT domain features, the separability of targets is improved, and the active sonar target classification with low SNR is realized. Experimental results show that the classification accuracy of the proposed method can reach more than 90% when the SNR is about 0 dB.
- Active sonar,
- FRactional Fourier Transform (FRFT),
- Feature fusion,
- Sparse representation,
- Target classification

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References(17)

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