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31 December 2019 Low-resolution ground surveillance radar target classification based on 1D-CNN
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The performance of radar automatic target recognition (ATR) highly depends on the quality of training database, the extracted features and classification algorithm. Radar target is detected by the Doppler effect in radar echo signal. Through processing the echo signals in different domains, the distinctive characteristic can be obtained intuitively. Furthermore, we can utilize the extracted features to complete radar target classification. This paper proposes a novel target recognition method based on 1D-convolution neural network (CNN) aiming at the ATR of low-resolution ground surveillance radar. The proposed approach uses 1D-CNN as feature extractor and softmax layer as classifier. We tested our method on actual collected database to classify human and car, which reached an accuracy of 98%. Compared with conventional artificial feature extraction approaches, our model shows better performance and adaptability.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Renhong Xie, Zeyu Sun, Huan Wang, Peng Li, Yibin Rui, Liyan Wang, and Chenguang Bian "Low-resolution ground surveillance radar target classification based on 1D-CNN", Proc. SPIE 11384, Eleventh International Conference on Signal Processing Systems, 113840R (31 December 2019);

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