Research Article
Parametric Sparse Recovery and SFMFT Based M-D Parameter Estimation with the Translational Component
@INPROCEEDINGS{10.1007/978-3-319-73447-7_16, author={Qi-fang He and Han-yang Xu and Qun Zhang and Yi-jun Chen}, title={Parametric Sparse Recovery and SFMFT Based M-D Parameter Estimation with the Translational Component}, 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={Compressive Sensing (CS) micro-Doppler effect (m-D effect) Parametric sparse representation Sinusoidal Frequency Modulated Fourier Transform (SFMFT) -resolution Fourier-Bessel (-FB) series Parameter estimation}, doi={10.1007/978-3-319-73447-7_16} }- Qi-fang He
Han-yang Xu
Qun Zhang
Yi-jun Chen
Year: 2018
Parametric Sparse Recovery and SFMFT Based M-D Parameter Estimation with the Translational Component
MLICOM
Springer
DOI: 10.1007/978-3-319-73447-7_16
Abstract
The micro-Doppler effect (m-D effect) provides unique signatures for target discrimination and recognition. In this paper, we consider a solution to the m-D parameter estimation. This method mainly consists of two procedures, with the first being the radar returns decomposition to extract the m-D components in Bessel domain. Then the parameter estimation issue is transformed as a parametric sparse recovery solution. A parametric sparse dictionary, which depends on m-D frequencies, is constructed according to the inherent property of the m-D returns. Considering that the m-D frequency is unknown, the discretizing m-D frequency range for the parametric dictionary matrix is calculated by the sinusoidal frequency modulated Fourier transform (SFMFT). In this manner, the finer m-D frequency, initial phases, maximum Doppler amplitudes and scattering coefficients are obtained by solving the sparse solution of the m-D returns. The simulation results verify the effectiveness.
