Small Target Detection Based on Frequency Domain Multichannel Graph Feature Perception on Sea Surface

XU Shuwen; JIAO Yinping; BAI Xiaohui; JIANG Junzheng

doi:10.11999/JEIT220188

Volume 45 Issue 5

May 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(5): 1567-1574

XU Shuwen, JIAO Yinping, BAI Xiaohui, JIANG Junzheng. Small Target Detection Based on Frequency Domain Multichannel Graph Feature Perception on Sea Surface[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1567-1574. doi: 10.11999/JEIT220188

Citation:

XU Shuwen, JIAO Yinping, BAI Xiaohui, JIANG Junzheng. Small Target Detection Based on Frequency Domain Multichannel Graph Feature Perception on Sea Surface[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1567-1574. doi: 10.11999/JEIT220188

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Small Target Detection Based on Frequency Domain Multichannel Graph Feature Perception on Sea Surface

doi: 10.11999/JEIT220188

1.
National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
2.
School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China

Funds: The National Natural Science Foundation of China (61871303, 62071346)

Received Date: 2022-02-25
Rev Recd Date: 2022-07-13

Available Online: 2022-07-19

Publish Date: 2023-05-10

Abstract

Abstract

The marine physical environment and electromagnetic environment are becoming increasingly complex, making the weak and slow small target detection in the sea clutter background be both emphasis and difficulty of radar target detection research. Due to small radar cross sections and low energy of small targets on the sea surface, traditional energy-based detection methods have a performance bottleneck. Feature-based detection methods focus on extracting distinguishing features between pure sea clutter and target returns to achieve target detection, which improve effectively the detection performance. Using the correlation of amplitude of radar returns in frequency domain, the graph theory method to feature-based detection is introduced. Firstly, measured sea clutter data are block-whitened to suppress sea clutter. Then, the data from Doppler channels are extracted in the frequency domain. With the help of graph processing methods, a distance adjacency matrix of the extracted data is constructed, and then it is converted into a Laplacian matrix. The maximum eigenvalue of Laplacian matrix under different radar returns is calculated, and fused with the relative Doppler peak height, then a new test statistic is obtained. By comparing the value of test statistics, sea clutter and returns with targets can be distinguished. Verified by the measured Ice multiParameter Imaging X-band (IPIX) database, the proposed detector attains better detection performance.
- Target detection,
- Sea clutter,
- Feature-based detection,
- Graph feature,
- Doppler channel

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References(21)

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