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

Reduction of Vibration-Induced Artifacts in Synthetic Aperture Radar Imagery

Target vibrations introduce nonstationary phase modulation, which is termed the micro-Doppler effect, into returned synthetic aperture radar (SAR) signals. This causes artifacts, or ghost targets, which appear near vibrating targets in reconstructed SAR images. Recently, a vibration estimation method based on the discrete fractional Fourier transform (DFrFT) has been developed. This method is capable of estimating the instantaneous vibration accelerations and vibration frequencies. In this paper, a deghosting method for vibrating targets in SAR images is proposed. For single-component vibrations, this method first exploits the estimation results provided by the DFrFT-based vibration estimation method to reconstruct the instantaneous vibration displacements. A reference signal, whose phase is modulated by the estimated vibration displacements, is then synthesized to compensate for the vibration-induced phase modulation in returned SAR signals before forming the SAR image. The performance of the proposed method with respect to the signal-to-noise and signalto-clutter ratios is analyzed using simulations. Experimental results using the Lynx SAR system show a substantial reduction in ghosting caused by a 1.5-cm 0.8-Hz target vibration in a true SAR image

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

SAR-Based Vibration Estimation Using the Discrete Fractional Fourier Transform

A vibration estimation method for synthetic aperture radar (SAR) is presented based on a novel application of the discrete fractional Fourier transform (DFRFT). Small vibrations of ground targets introduce phase modulation in the SAR returned signals. With standard preprocessing of the returned signals, followed by the application of the DFRFT, the time-varying accelerations, frequencies, and displacements associated with vibrating objects can be extracted by successively estimating the quasi-instantaneous chirp rate in the phase-modulated signal in each subaperture. The performance of the proposed method is investigated quantitatively, and the measurable vibration frequencies and displacements are determined. Simulation results show that the proposed method can successfully estimate a two-component vibration at practical signal-to-noise levels. Two airborne experiments were also conducted using the Lynx SAR system in conjunction with vibrating ground test targets. The experiments demonstrated the correct estimation of a 1-Hz vibration with an amplitude of 1.5 cm and a 5-Hz vibration with an amplitude of 1.5 mm

Clutter Suppression via Hankel Rank Reduction for DFrFT-Based Vibrometry Applied to SAR

Hankel rank reduction (HRR) is a method that, by prearranging the data in a Hankel matrix and performing rank reduction via singular value decomposition, suppresses the noise of a time-history vector comprised of the superposition of a finite number of sinusoids. In this letter, the HRR method is studied for performing clutter suppression in synthetic aperture radar (SAR)-based vibrometry. Specifically, three different applications of the HRR method are presented. First, resembling the SAR slow-time signal model, the HRR method is utilized for separating a chirp signal immersed in a sinusoidal clutter. Second, using simulated airborne SAR data with 10 dB of signal-to-clutter ratio, the HRR method is applied to perform target isolation and to improve the results of an SAR-based vibration estimation algorithm. Finally, the vibrometry approach combined with the HRR method is validated using actual airborne SAR data

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

SAR moving target imaging in a sparsity-driven framework

In synthetic aperture radar (SAR) imaging, sparsity-driven imaging techniques have been shown to provide high resolution images with reduced sidelobes and reduced speckle, by allowing the incorporation of prior information about the scene into the problem. Just like many common SAR imaging methods, these techniques also assume the targets in the scene are stationary over the data collection interval. Here, we consider the problem of imaging in the presence of targets with unknown motion in the scene. Moving targets cause phase errors in the SAR data and these errors lead to defocusing in the corresponding spatial region in the reconstructed image. We view phase errors resulting from target motion as errors on the observation model of a static scene. Based on these observations we propose a method which not only benefits from the advantages of sparsity-driven imaging but also compansates the errors arising due to the moving targets. Considering that in SAR imaging the underlying scene usually admits a sparse representation, a nonquadratic regularization-based framework is used. The proposed method is based on minimization of a cost function which involves regularization terms imposing sparsity on the reflectivity field to be imaged, as well as on the spatial structure of the motion-related phase errors, reflecting the assumption that only a small percentage of the entire scene contains moving targets. Experimental results demonstrate the effectiveness of the proposed approach in reconstructing focused images of scenes containing multiple targets with unknown motion

Marine targets recognition through micro-motion estimation from SAR data

The capability to perform Automatic Target Recognition (ATR) from SAR images has great importance for both civilian and military applications. However, this task becomes challenging when the quality and quantity of target information is not sufficient to reliably discriminate the targets. This is particularly important when dealing with marine targets, where features such as scattering intensities and shapes are common to many different targets. This paper investigates the possibility to enhance classification capabilities of marine targets in SAR images by exploiting the micro-motion information. This characterizing source of information, is extracted by applying Doppler sub-apertures and pixel tracking on SAR images containing the target of interest. The proposed approach is validated on real COSMO-SkyMed SAR data demonstrating the effectiveness to discriminate ships through their unique Doppler fingerprint

Generic Radar Processing Methods for Monitoring Tasks on Bridge Infrastructure

Kritische Verkehrsinfrastrukturen, wie z. B. Brücken, können nur dann sicher betrieben werden, wenn ihr Zustand regelmäßig bewertet wird. Neben visuellen Inspektionen umfasst die Bewertung auch Messungen des Brückenverhaltens auf statische oder dynamische Lasten. Diese Messungen werden in der Regel mit einer Vielzahl von Sensoren durchgeführt, die direkt an der Brücke befestigt sind. Zunehmend werden jedoch auch Fernerkundungssensoren eingesetzt, wie z.B. das bodenbasierte interferometrische Radar (engl.: ground-based interferometric radar - GBR). GBR können aus der Ferne Verschiebungen mit einer Genauigkeit im Submillimeterbereich messen, indem sie eine elektromagnetische Welle aussenden, die von Strukturen an der Unterseite der Brücke reflektiert wird. Im Vergleich zu direkt befestigten Sensoren wird die Installationszeit verkürzt und der normale Betrieb der Brücke wird nicht beeinträchtigt. Vergleichbare Messunsicherheiten lassen sich jedoch nur erreichen, wenn bei der Prozessierung der Messungen bestimmte Herausforderungen berücksichtigt werden. Dabei geht es vor allem um die Entfernung externer Einflüsse wie Störungen des Signals oder Veränderungen atmosphärischer Parameter. Die Messungen werden außerdem durch statischen Clutter und Projektionsfehler beeinflusst, die zu systematischen Abweichungen führen. Statischer Clutter wird mit einer angepassten Kreisschätzung bestimmt, während Projektionsfehler durch die Verwendung mehrerer Sensoren zur Schätzung separater Verschiebungskomponenten vermindert werden. Mit diesen zusätzlichen Prozessierungsschritten erreicht GBR eine ähnliche Unsicherheit wie andere Fernerkundungssensoren, was durch Vergleiche mit Referenzsensoren validiert wird. Verbleibende Unterschiede zu diesen Referenzsensoren lassen sich durch Unsicherheiten bei der Schätzung von Clutter und durch die begrenzte Auflösung einzelner Reflexionen erklären. Die resultierenden Verschiebungsmessungen werden dann zur Schätzung schadensempfindlicher Merkmale wie Eigenfrequenzen und Eigenformen verwendet. Eigenfrequenzen werden bestimmt, indem ein Modell einer gedämpften Sinuskurve für die Schwingung nach einer Fahrzeugüberfahrt geschätzt wird. Mit diesem Ansatz wird jede Fahrzeugüberfahrt separat analysiert, was eine Unterscheidung zwischen verschiedenen Fahrzeugmassen ermöglicht. Außerdem erlaubt die große Anzahl von Frequenzschätzungen eine zuverlässigere Bestimmung des Temperatureinflusses auf die Eigenfrequenzen. Für die Bestimmung der Eigenformen wird ein alternativer Messaufbau erarbeitet. Dieser Aufbau nutzt die flache Unterseite einer Brücke, um das ausgesendete Signal auf einen Reflektor auf dem Boden zu spiegeln. Eine permanente Installation von Reflektoren an der Brückenunterseite ist daher nicht erforderlich, wodurch die Anwendung von GBR auf eine große Anzahl von Brücken erweitert wird. Darüber hinaus kann die Messung nicht durch andere Verschiebungskomponenten beeinflusst werden, was das Auftreten von systematischen Abweichungen verringert. Folglich sind die Eigenformen empfindlicher gegenüber Schäden, da die Unsicherheiten reduziert werden. Das zugrunde liegende Prinzip dieses alternativen Messaufbaus wird wiederum durch Vergleiche mit Referenzsensoren validiert