Abstract
The Doppler spectrums of radar echoes of targets can reflect the change of the instantaneous velocity of targets. Therefore, it can be used for analyzing the motion state of the target and classifying them. Besides, deep learning is widely used in the classification of images. This paper proposes a deep learning based method of classifying targets in sea clutter. First, we introduce the motion model of targets and analyze their Doppler spectrum, based on which, we stimulate the time–frequency images of targets’ radar echoes. Since clutters in echoes usually obey Weibull distribution, we add Weibull clutter (Mezache and Soltani) to a novel threshold optimization technique for far-away detection in Weibull clutter using fuzzy neural networks, 2007, [1]) to the echo signals. Then we classify targets with different networks using NVIDIA DIGITS, based on the images and analyze the results of classification.
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Acknowledgements
This work was supported in part by The National Natural Science Foundation of China (61871391, U1633122, 61871392, 61531020), Scientific Research Development of Shandong (J17KB139), and Young Elite Scientist Sponsorship Program of CAST (YESS20160115).
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Su, N., Chen, X., Mou, X., Zhang, L., Guan, J. (2020). A Deep Learning Method of Moving Target Classification in Clutter Background. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 517. Springer, Singapore. https://doi.org/10.1007/978-981-13-6508-9_38
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DOI: https://doi.org/10.1007/978-981-13-6508-9_38
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