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.
This work was supported by the National Natural Science Foundation of China under Grant 61631019 and 61471386.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Wang, D.C.: An overview of micro-doppler radar. J. CAEIT 7(6), 575–580 (2012). (in Chinese)
Ruegg, M., Meier, E., Nuesch, D.: Vibration and rotation in millimeter-wave SAR. IEEE Trans. Geosci. Remote Sens. 45(2), 293–304 (2007)
Liu, Z., Wei, X.Z., Li, X.: Aliasing-free micro-doppler analysis based on short-time compressed sensing. IET Sig. Process. 8(2), 176–187 (2014)
Suresh, P., Thayaparan, T., Obulesu, T., Venkataramaniah, K.: Extracting micro-doppler radar signatures from rotating targets using fourier-bessel transform and time-frequency analysis. IEEE Trans. Geosci. Remote Sens. 52(6), 3204–3210 (2014)
He, Q.F., Wang, J.D., Wang, K., Wu, Y.G., Zhang, Q.: Multi-component LFM signals detection and separation using fourier-bessel series expansion. In: RADAR 2016 Conference, Guangzhou, pp. 1749–1753. IEEE Press (2016)
Peng, B., Wei, X.Z., Deng, B., Chen, H.W., Liu, Z., Li, X.: A sinusoidal frequency modulation fourier transform for radar-based vehicle vibration estimation. IEEE Trans. Instrument. Measure. 63(9), 2188–2199 (2014)
Luo, Y., Zhang, Q., Wang, G.Z., Guan, H., Bai, Y.Q.: Micro-motion signature extraction method for wideband radar based on complex image OMP decomposition. J. Radars. 1(4), 361–369 (2012). (in Chinese)
Orovic, I., Stankovic, S., Thayaparan, T.: Time-frequency-based instantaneous frequency estimation of sparse signals from incomplete set of samples. IET Sig. Process. 8(3), 239–245 (2014)
Stankovic, L., Orovic, I., Stankovic, S., Amin, M.: Compressive sensing based separation of nonstationary and stationary signals overlapping in time-frequency. IEEE Trans. Sig. Process. 61(18), 4562–4572 (2013)
Flandrin, P., Borgnat, P.: Time-frequency energy distributions meet compressed sensing. IEEE Trans. Sig. Process. 58(6), 2974–2982 (2010)
Chen, X.L., Dong, Y.L., Huang, Y., Guan J.: Detection of marine target with quadratic modulated frequency micromotion signature via morphological component analysis. In: 3rd International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, Pisa, Italy, pp. 220–224 (2015)
Stankovic, S., Orovic, I., Pejakovic, T., Orovic, M.: Compressive sensing reconstruction of signals with sinusoidal phase modulation: application to radar micro-doppler. In: 22nd Telecommunications forum TELFOR, Belgrade, pp. 565–568 (2014)
Orovic, I., Stankovic, S., Amin, M.: Compressive sensing for sparse time-frequency representation of nonstationary signals in the presence of impulsive noise. In: Proceedings of SPIE, vol. 8717, United States (2013)
Li, G., Varshney, P.K.: Micro-doppler parameter estimation via parametric sparse representation and pruned orthogonal matching pursuit. IEEE J. Sel. Topic. Appl. Earth Obs. Remote Sens. 7(12), 4937–4948 (2014)
Xia, P., Wan, X.R., Yi, J.X.: Micromotion parameters estimation for rotating structures on target in passive radar. Chin. J Radio Sci. 31(4), 676–682 (2016)
Hasar, U.C., Barroso, J.J., Sabah, C., Kaya, Y.: Resolving phase ambiguity in the inverse problem of reflection-only measurement methods. Progr. Electromagn. Res. 129, 405–420 (2012)
Tropp, J.A., Gilbert, A.C.: Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 53(12), 4655–4666 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
He, Qf., Xu, Hy., Zhang, Q., Chen, Yj. (2018). Parametric Sparse Recovery and SFMFT Based M-D Parameter Estimation with the Translational Component. 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_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-73447-7_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73446-0
Online ISBN: 978-3-319-73447-7
eBook Packages: Computer ScienceComputer Science (R0)