A Method for Detecting Small Slow Targets in Sea Surface Based on Diagonal Integrated Bispectrum

GUAN Jian; WU Xijie; DING Hao; LIU Ningbo; DONG Yunlong; ZHANG Pengfei

doi:10.11999/JEIT210408

Volume 44 Issue 7

Jul. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(7): 2449-2460

GUAN Jian, WU Xijie, DING Hao, LIU Ningbo, DONG Yunlong, ZHANG Pengfei. A Method for Detecting Small Slow Targets in Sea Surface Based on Diagonal Integrated Bispectrum[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2449-2460. doi: 10.11999/JEIT210408

Citation:

GUAN Jian, WU Xijie, DING Hao, LIU Ningbo, DONG Yunlong, ZHANG Pengfei. A Method for Detecting Small Slow Targets in Sea Surface Based on Diagonal Integrated Bispectrum[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2449-2460. doi: 10.11999/JEIT210408

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A Method for Detecting Small Slow Targets in Sea Surface Based on Diagonal Integrated Bispectrum

doi: 10.11999/JEIT210408

1.
Naval Aviation University, Yantai 264001, China
2.
National Innovation Institute of Defense Technology, Academy of Military Science, Beijing 100071, China
3.
Unit 92975 of the PLA

Funds: The National Natural Science Foundation of China (62101583, 61871392, 61871391)

Received Date: 2021-05-12
Rev Recd Date: 2021-09-28

Available Online: 2021-10-01

Publish Date: 2022-07-25

Abstract

Abstract

Considering the technical difficulty of radar to detect small targets embedded in the sea clutter, a three-feature fusion detection method based on diagonal integrated bispectrum is proposed. Firstly, the diagonal integrated bispectrum is obtained from the estimated bispectrum of the signal to be detected. Then, according to the nonlinear coupling difference between sea clutter cell and target cell, three features consist of peak value, centroid frequency and spectrum width are extracted from the diagonal integrated bispectrum. Considering that the number of coherent pulses used by radar in scanning mode is usually small, it is easy to lead to feature instability, and then affect the separability of sea clutter and target. For this reason, through the comprehensive application of multi-frame scanning historical data and current frame data, three cumulative features including cumulative peak value, total variation, cumulative spectrum width are obtained by accumulating three spectrum features. Finally, the convex hull classification algorithm is used to perform fusion detection in three dimensional feature space. The measured CSIR dataset verifies that, under same parameters, the proposed detection method has better detection performance compared with the existing detection methods based on three time-frequency features, amplitude feature and doppler features, fractal feature.
- Target detection,
- Sea clutter,
- Diagonal integrated bispectrum,
- Three features

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References(27)

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