Active False Target Identification Method Based on Frequency Response Features Clustering in Multi-Coherent Processing Intervals

WEI Wenbin; PENG Ruihui; SUN Dianxing; TAN Shuncheng; SONG Yingjuan; ZHANG Jialin

doi:10.11999/JEIT231012

Volume 46 Issue 7

Jul. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(7): 2721-2731

WEI Wenbin, PENG Ruihui, SUN Dianxing, TAN Shuncheng, SONG Yingjuan, ZHANG Jialin. Active False Target Identification Method Based on Frequency Response Features Clustering in Multi-Coherent Processing Intervals[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2721-2731. doi: 10.11999/JEIT231012

Citation:

WEI Wenbin, PENG Ruihui, SUN Dianxing, TAN Shuncheng, SONG Yingjuan, ZHANG Jialin. Active False Target Identification Method Based on Frequency Response Features Clustering in Multi-Coherent Processing Intervals[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2721-2731. doi: 10.11999/JEIT231012

Citation:

WEI Wenbin, PENG Ruihui, SUN Dianxing, TAN Shuncheng, SONG Yingjuan, ZHANG Jialin. Active False Target Identification Method Based on Frequency Response Features Clustering in Multi-Coherent Processing Intervals[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2721-2731. doi: 10.11999/JEIT231012

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Active False Target Identification Method Based on Frequency Response Features Clustering in Multi-Coherent Processing Intervals

doi: 10.11999/JEIT231012

1.
Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao 266000, China
2.
Institute of Information Fusion, Navy Aeronautical University, Yantai 264001, China

Funds: China Aerospace Science and Technology Corporation Stabilization Support Project (ZY0110020009), China Postdoctoral Science Foundation (2021M693003), The National Natural Science Foundation of China (61731023)

Received Date: 2023-09-18
Rev Recd Date: 2023-11-30

Available Online: 2023-12-06

Publish Date: 2024-07-29

Abstract

Abstract

Most of the existing intelligent algorithms for identifying real and false targets are based on supervised learning and perform poorly under a low signal-to-noise ratio. Considering the above problems, an unsupervised clustering identification method of real and false targets based on frequency response features in multi-Coherent Processing Intervals(CPIs) is proposed by using the variability and uniqueness of the scattering characteristics of real and false targets in multi-CPIs, respectively. Firstly, the real and false targets are windowed and truncated along the fast time dimension in the fast-slow time domain, and the fast-slow time domain frequency response features are extracted to construct a preliminary sample set. Then, the real and false targets are identified by a two-step recognition algorithm composed of an Agglomerative clustering and a feature fusion network. Finally, a multi-CPI joint decision method is proposed to improve the recognition performance and reliability. It is proved by simulation and measured data that the proposed method can effectively identify real targets and multiple active false targets.
- Active false target,
- Multi Coherent Processing Intervals(CPIs),
- Scattering characteristics,
- Fast-slow time domain frequency response,
- Unsupervised,
- Agglomerative clustering

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References(25)

References

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