Abstract
In order to effectively improve radar detection ability of moving target under the conditions of strong clutter and complex motion characteristics, the principle framework of Short-Time sparse Time-Frequency Distribution (ST-TFD) is established combing the advantages of TFD and sparse representation. Then, Short-Time Sparse FRactional Ambiguity Function (ST-SFRAF) method is proposed and applied to radar micro-Doppler (m-D) detection and extraction. It is verified by real radar data that the proposed methods can achieve high-resolution and low complexity TFD of time-varying signal in time-sparse domain, and has the advantages of good time-frequency resolution, anti-clutter, and so on. It can be expected that the proposed methods can provide a novel solution for time-varying signal analysis and radar moving target detection.
This work was supported in part by the National Natural Science Foundation of China (61501487, 61401495, U1633122, 61471382, 61531020), National Defense Science and Technology Fund (2102014), Young Elite Scientist Sponsorship Program of CAST (YESS20160115) and Special Funds of Taishan Scholars of Shandong.
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Chen, X., Yu, X., Guan, J., He, Y. (2018). High-Resolution Sparse Representation of Micro-Doppler Signal in Sparse Fractional Domain. In: Gu, X., Liu, G., Li, B. (eds) Machine Learning and Intelligent Communications. MLICOM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 227. Springer, Cham. https://doi.org/10.1007/978-3-319-73447-7_26
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