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

Remote Vibration Estimation Using Displaced Phase Center Antenna SAR in a Strong Clutter Environment

Synthetic aperture radar (SAR) is a ubiquitous remote sensing platform that is used for numerous applications. In its most common con\ufb01guration, SAR produces a high resolution, two-dimensional image of a scene of interest. An underlying assumption when creating this high resolution image is that all targets in the ground scene are stationary throughout the duration of the image collection. If a target is not static, but instead vibrating, it introduces a modulation on the returned radar signal termed the micro-Doppler e\ufb00ect. The ability to estimate the targets vibration frequency and vibration amplitude by exploiting the micro-Doppler e\ufb00ect, all while in a high clutter environment can provide strategic information for target identi\ufb01cation and target condition/status. This thesis discusses one method that processes the non-stationary signal of interest generated by the vibrating target in displaced phase center antenna (DPCA)-SAR in high clutter. The method is based on the extended Kalman \ufb01lter (EKF) \ufb01rst proposed by Dr. Wang in his PhD dissertation titled Time-frequency Methods for Vibration Estimation Using Synthetic Aperture Radar [24]. Previously, EKF method could accurately estimate the target\u27s vibration frequency for single component sinusoidal vibrations. In addition, the target\u27s vibration amplitude and position could be tracked throughout the duration of the aperture for single component sinusoidal vibrations. This thesis presents a modi\ufb01cation to the EKF method, which improves the EKF method\u27s overall performance. This modi\ufb01cation improves the tracking capability of single component vibrations and provides reliable position tracking for several other di\ufb00erent types of vibration dynamics. In addition, the EKF method is more reliable at higher noise levels. More speci\ufb01cally, for a single component vibration, the mean square error (MSE) of the original method is .2279, while the MSE of the method presented in this paper is .1503. Therefore, the method presented in this paper improves the position estimate of the vibrating target by 34% when SNR = 15 dB. For the multicomponent vibrations, the mean square error of the estimated target position is reduced b 76% when SNR = 15 dB. The original EKF method and the modi\ufb01ed EKF method as well as simulations for various target vibration dynamics are provided in this thesis.\u2

Detection and classification of vibrating objects in SAR images

The vibratory response of buildings and machines contains key information that can be exploited to infer their operating conditions and to diagnose failures. Furthermore, since vibration signatures observed from the exterior surfaces of structures are intrinsically linked to the type of machinery operating inside of them, the ability to monitor vibrations remotely can enable the detection and identification of the machinery. This dissertation focuses on developing novel techniques for the detection and M-ary classification of vibrating objects in SAR images. The work performed in this dissertation is conducted around three central claims. First, the non-linear transformation that the micro-Doppler return of a vibrating object suffers through SAR sensing does not destroy its information. Second, the instantaneous frequency (IF) of the SAR signal has sufficient information to characterize vibrating objects. Third, it is possible to develop a detection model that encompasses multiple scenarios including both mono-component and multi-component vibrating objects immersed in noise and clutter. In order to cement these claims, two different detection and classification methodologies are investigated. The first methodology is data-driven and utilizes features extracted with the help of the discrete fractional Fourier transform (DFRFT) to feed machine-learning algorithms (MLAs). Specifically, the DFRFT is applied to the IF of the slow-time SAR data, which is reconstructed using techniques of time-frequency analysis. The second methodology is model-based and employs a probabilistic model of the SAR slow-time signal, the Karhunen-Loève transform (KLT), and a likelihood-based decision function. The performance of the two proposed methodologies is characterized using simulated data as well as real SAR data. The suitability of SAR for sensing vibrations is demonstrated by showing that the separability of different classes of vibrating objects is preserved even after non-linear SAR processing Finally, the proposed algorithms are studied when the range-compressed phase-history data is contaminated with noise and clutter. The results show that the proposed methodologies yields reliable results for signal-to-noise ratios (SNRs) and signal-to-clutter ratios (SCRs) greater than -5 dB. This requirement is relaxed to SNRs and SCRs greater than -10 dB when the range-compressed phase-history data is pre-processed with the Hankel rank reduction (HRR) clutter-suppression technique