Research Article
High-Resolution Sparse Representation of Micro-Doppler Signal in Sparse Fractional Domain
@INPROCEEDINGS{10.1007/978-3-319-73447-7_26, author={Xiaolong Chen and Xiaohan Yu and Jian Guan and You He}, title={High-Resolution Sparse Representation of Micro-Doppler Signal in Sparse Fractional Domain}, proceedings={Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part II}, proceedings_a={MLICOM}, year={2018}, month={2}, keywords={Sparse representation Micro-Doppler signal Sparse time-frequency distribution (STFD) Short-time sparse fractional ambiguity function (ST-SFRAF)}, doi={10.1007/978-3-319-73447-7_26} }- Xiaolong Chen
Xiaohan Yu
Jian Guan
You He
Year: 2018
High-Resolution Sparse Representation of Micro-Doppler Signal in Sparse Fractional Domain
MLICOM
Springer
DOI: 10.1007/978-3-319-73447-7_26
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.
