Remote Vibration Estimation Using Displaced-Phase-Center Antenna SAR for Strong Clutter Environments

It has been previously demonstrated that it is possible to perform remote vibrometry using synthetic aperture radar (SAR) in conjunction with the discrete fractional Fourier transform (DFrFT). Specifically, the DFrFT estimates the chirp parameters (related to the instantaneous acceleration of a vibrating object) of a slow-time signal associated with the SAR image. However, ground clutter surrounding a vibrating object introduces uncertainties in the estimate of the chirp parameter retrieved via the DFrFT method. To overcome this shortcoming, various techniques based on subspace decomposition of the SAR slow-time signal have been developed. Nonetheless, the effectiveness of these techniques is limited to values of signal-to-clutter ratio ≥5 dB. In this paper, a new vibrometry technique based on displaced-phase-center antenna (DPCA) SAR is proposed. The main characteristic of a DPCA-SAR is that the clutter signal can be canceled, ideally, while retaining information on the instantaneous position and velocity of a target. In this paper, a novel method based on the extended Kalman filter (EKF) is introduced for performing vibrometry using the slow-time signal of a DPCA-SAR. The DPCA-SAR signal model for a vibrating target, the mathematical characterization of the EKF technique, and vibration estimation results for various types of vibration dynamics are presented

Multicomponent AM-FM energy demodulation with applications to signal processing and communications

Ph.D.Petros Marago

Fractional spectrogram for characterizing and classifying vibrating objects in SAR images

A recently developed improved spectrogram that uses the discrete fractional Fourier transform (DFRFT) is used to retrieve the vibration signature that represents targets in synthetic aperture radar (SAR) data. The retrieved signature is used as input to a feature extraction process, which characterizes the vibration waveform using the DFRFT as well as histograms and statistics. The study of the performance of two classifiers, one trained with features extracted from vibration measurements and the other trained with feature extracted from simulated SAR data generated from the same vibration measurements, validates the suitability of the DFRFT-based spectrogram for retrieving and characterizing the dynamics of vibrating objects in SAR images