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Peng Lei, Jun Wang, Jinping Sun Beijing University of Aeronautics and Astronautics

Radar Micro-Doppler Analysis and Rotation Parameter Estimation for Rigid Targets with Complicated Micro-Motions. Peng Lei, Jun Wang, Jinping Sun Beijing University of Aeronautics and Astronautics IGARSS 2011, Vancouver, Canada July 26, 2011. Outline. Introduction

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Peng Lei, Jun Wang, Jinping Sun Beijing University of Aeronautics and Astronautics

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  1. Radar Micro-Doppler Analysis and Rotation Parameter Estimation for Rigid Targets with Complicated Micro-Motions Peng Lei, Jun Wang, Jinping Sun Beijing University of Aeronautics and Astronautics IGARSS 2011, Vancouver, Canada July 26, 2011

  2. Outline • Introduction • Spectral Analysis of Micro-Doppler Frequency • Inertial Model • Spectral Structure • Estimation Methodology • Results • Conclusion IGARSS 2011

  3. Introduction • Background Micro-Doppler (mD) effect -- the frequency modulation phenomenon in radar echoes caused by objects’ micro-motions attitude dynamics classification limb/respiratory movement micro-motions mD effect micro-motion parameters … engine vibration/ wheel rotation EXPLORE IGARSS 2011

  4. Introduction • Objective of our work • Free symmetric rigid bodies with single scattering center • Micro-dynamic characteristics • select rotation parameters to represent them • Effect on the mD • non-sinusoidal variation of the mD frequency • MD-based parameter estimation of their attitude dynamics IGARSS 2011

  5. Spectral Analysis of MD Frequency • Inertial model • Objects’ attributes Micro-motion states MD echoes • For the axisymmetric body ( ), the three attitude angles are given by: • spin angle: • precession angle: • nutation angle: moments of inertia initial rotation state attitude angles (at any time t) Rot(t) signal model kinematic equations mD echoes linear time variant constant IGARSS 2011

  6. Spectral Analysis of MD Frequency • Inertial model • Characteristics of the micro-motion • spin rate: • precession rate: where are moments of inertia, are initial rotational velocities, and is the total angular momentum. • this is well-known as the precession motion rotation parameters precession of a gyroscope from http://en.wikipedia.org/wiki/Precession IGARSS 2011

  7. Spectral Analysis of MD Frequency • Spectral structure of mD time-frequency sequence • Micro-motions have an great effect on the time variation of instantaneous mD frequency • The mD frequency of radar echoes is expressed as IGARSS 2011

  8. Spectral Analysis of MD Frequency • Spectral structure of mD time-frequency sequence • Considering the inertial model and constant terms, the mD frequency from the scatterer on a free rigid body can be rewritten as • HERE, behaves as a frequency function of the time t linear sum of four sinusoidal components IGARSS 2011

  9. Spectral Analysis of MD Frequency • Spectral structure of mD time-frequency sequence • Amplitudes and constant phases in are invariant , which are with respect to , , x, y, z, et al. • Frequencies of the four sinusoi-dal components correspond to the rotation parameters, and IGARSS 2011

  10. Estimation Methodology • KEY: the mD time-frequency features • Process to estimate the rotation parameters Time-frequency analysis (Short Time Fourier Transform) Formation of mD time-frequency sequence Spectral estimation radar mD echoes time-frequency sequence spectral estimation rotation parameters spectrogram STFT RELAX IGARSS 2011 mapping

  11. Estimation Methodology • Time-frequency analysis (STFT) • Formation of mD time-frequency sequence • Morphological processing • Location mapping of “target” points f g(ti) h(tm,fn) amplitude frequency t time t time one-dimensional (1D) sampled data two-dimensional (2D) matrix data f frequency r(tk) t time 1D sequence data IGARSS 2011

  12. Estimation Methodology • Spectral estimation • The RELAX algorithm is an asymptotic maximum likelihood approach based on the Fourier transform amplitude IGARSS 2011 frequency

  13. Simulation Results • Simulation conditions micro-motion trajectory in 3D space theoretical mD frequency IGARSS 2011

  14. Simulation Results • Spin rate estimates in Monte-Carlo simulations 1. theoretical values – calculation results 2. ideal values – simulation results under noise-free condition 3. estimation values – Monte-Carlo results at given SNR level when SNR>13dB, accuracy>98% IGARSS 2011

  15. Simulation Results • Precession rate estimates in Monte-Carlo simulations when SNR>13dB, accuracy>91% IGARSS 2011

  16. Conclusion • Free symmetric rigid objects generally take the precession motion, which has two important rotation parameters, i.e., spin rate and precession rate • Their mD frequency data sequence (1D) is composed of four sinusoidal components with respect to the spin and precession rates • The proposed method could achieve the estimation of rotation parameters under noise environment • Current exploration is extending to the multi-scatterer objects, which is more complex and needs more work IGARSS 2011

  17. Thank you IGARSS 2011

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