Abstract
Current methods for radar target detection usually work on the basis of high signal-to-clutter ratios. In this paper we propose a novel convolutional neural network based dual-activated clutter suppression algorithm, to solve the problem caused by low signal-to-clutter ratios in actual situations on the sea surface. Dual activation has two steps. First, we multiply the activated weights of the last dense layer with the activated feature maps from the upsample layer. Through this, we can obtain the class activation maps (CAMs), which correspond to the positive region of the sea clutter. Second, we obtain the suppression coefficients by mapping the CAM inversely to the sea clutter spectrum. Then, we obtain the activated range-Doppler maps by multiplying the coefficients with the raw range-Doppler maps. In addition, we propose a sampling-based data augmentation method and an effective multiclass coding method to improve the prediction accuracy. Measurement on real datasets verified the effectiveness of the proposed method.
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Zhi-yong SONG and Guan-qing LI designed the research. Guan-qing LI processed the data and drafted the manuscript. Qiang FU helped organize the manuscript. Guan-qing LI and Zhi-yong SONG revised and finalized the paper.
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Guan-qing LI, Zhi-yong SONG, and Qiang FU declare that they have no conflict of interest.
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Li, Gq., Song, Zy. & Fu, Q. A convolutional neural network based approach to sea clutter suppression for small boat detection. Front Inform Technol Electron Eng 21, 1504–1520 (2020). https://doi.org/10.1631/FITEE.1900523
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DOI: https://doi.org/10.1631/FITEE.1900523
Key words
- Convolutional neural networks
- Class activation map
- Short-time Fourier transform
- Small target detection
- Sea clutter suppression