SAR-based vibrometry using the fractional Fourier transform

Justin B. Campbell; Qi Wang; Jelili Ade-Bello; Humberto Caudana; Nicole B. Trujillo; Ishwor Bhatta; Ralf Dunkel; Thomas Atwood; Armin Doerry; Walter H. Gerstle; Balu Santhanam; Majeed M. Hayat

doi:10.1117/12.2180660

26 May 2015 SAR-based vibrometry using the fractional Fourier transform

Justin B. Campbell, Qi Wang, Jelili Ade-Bello, Humberto Caudana, Nicole B. Trujillo, Ishwor Bhatta, Ralf Dunkel, Thomas Atwood, Armin Doerry, Walter H. Gerstle, Balu Santhanam, Majeed M. Hayat

Author Affiliations +

Proceedings Volume 9461, Radar Sensor Technology XIX; and Active and Passive Signatures VI; 94610O (2015) https://doi.org/10.1117/12.2180660
Event: SPIE Defense + Security, 2015, Baltimore, MD, United States

Abstract

A fundamental assumption when applying Synthetic Aperture Radar (SAR) to a ground scene is that all targets are motionless. If a target is not stationary, but instead vibrating in the scene, it will introduce a non-stationary phase modulation, termed the micro-Doppler effect, into the returned SAR signals. Previously, the authors proposed a pseudosubspace method, a modification to the Discrete Fractional Fourier Transform (DFRFT), which demonstrated success for estimating the instantaneous accelerations of vibrating objects. However, this method may not yield reliable results when clutter in the SAR image is strong. Simulations and experimental results have shown that the DFRFT method can yield reliable results when the signal-to-clutter ratio (SCR) > 8 dB. Here, we provide the capability to determine a target's frequency and amplitude in a low SCR environment by presenting two methods that can perform vibration estimations when SCR < 3 dB. The first method is a variation and continuation of the subspace approach proposed previously in conjunction with the DFRFT. In the second method, we employ the dual-beam SAR collection architecture combined with the extended Kalman filter (EKF) to extract information from the returned SAR signals about the vibrating target. We also show the potential for extending this SAR-based capability to remotely detect and classify objects housed inside buildings or other cover based on knowing the location of vibrations as well as the vibration histories of the vibrating structures that house the vibrating objects.

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Justin B. Campbell, Qi Wang, Jelili Ade-Bello, Humberto Caudana, Nicole B. Trujillo, Ishwor Bhatta, Ralf Dunkel, Thomas Atwood, Armin Doerry, Walter H. Gerstle, Balu Santhanam, and Majeed M. Hayat "SAR-based vibrometry using the fractional Fourier transform", Proc. SPIE 9461, Radar Sensor Technology XIX; and Active and Passive Signatures VI, 94610O (26 May 2015); https://doi.org/10.1117/12.2180660

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