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This paper provides analytical principles to relate the signature of a moving target to parameters in a SAR system. Our objective is to establish analytical tools that could predict the shift and smearing of a moving target in a subaperture SAR image. Hence, a user could identify the system parameters such as the coherent processing interval for a subaperture that is suitable to localize the signature of a moving target for detection, tracking and geolocating the moving target. The paper begins by outlining two well-known SAR data collection methods to detect moving targets. One uses a scanning beam in the azimuth domain with a relatively high PRF to separate the moving targets and the stationary background (clutter); this is also known as Doppler Beam Sharpening. The other scheme uses two receivers along the track to null the clutter and, thus, provide GMTI. We also present results on implementing our SAR-GMTI analytical principles for the anticipated shift and smearing of a moving target in a simulated code. The code would provide a tool for the user to change the SAR system and moving target parameters, and predict the properties of a moving target signature in a subaperture SAR image for a scene that is composed of both stationary and moving targets. Hence, the SAR simulation and imaging code could be used to demonstrate the validity and accuracy of the above analytical principles to predict the properties of a moving target signature in a subaperture SAR image.

Question: A 73-year-old Asian-American woman with a history of hypertension, non–insulin-dependent diabetes mellitus, and obstructive sleep apnea presented to the emergency department with acute-onset epigastric pain associated with nausea, vomiting, diaphoresis, and lightheadedness. The pain was severe, sharp, and nonradiating, with minimal response to intravenous (IV) morphine. She denied fevers, changes in appetite, hematemesis, hematochezia, melena, diarrhea, or constipation. There was no history of trauma. She did not smoke or drink alcohol. On examination, the patient was in obvious distress from pain; her blood pressure was 151/58, pulse 83, and there was diffuse abdominal tenderness but no distention, rebound, or guarding. Labs were notable for negative serial troponins, normal white count, hemoglobin, and prothrombin time/International Normalized Ratio, elevated transaminases (aspartate aminotransferase, 284; alanine aminotransferase, 313), elevated alkaline phosphatase (102), a total bilirubin of 1.2 (0.2 direct), and normal amylase and lipase levels. Computed tomography (CT) of the abdomen and pelvis with IV contrast (Figure A) revealed a 3 9 6 cm irregular, hyperdense mass in the right anterior pararenal space inseparable from the descending portion of the duodenum. The pancreas was normal in size, contour, and density; no solid or cystic pancreatic masses were seen. Esophagogastroduodenoscopy revealed no evidence of lumenal disease. What is the etiology of this patient’s condition? Look on page 505 for the answer and see the GASTROENTEROLOGY web site (www.gastrojournal.org) for more information n submitting your favorite image to Clinical Challenges and Images in GI.

This paper discusses the design and implementation of a synthetic aperture acoustic imaging system for investigating solid objects in an outdoor environment. Measurement results are reported for a setup consisting of a rail-mounted transceiver, digital data acquisition and control system, and local storage with data post processing capabilities. This imaging system works much like typical sonar and RF synthetic aperture radar technologies but operates above ground and uses acoustic waves in air for its interrogation.

To show the signal intensity varies with shot number in diffusion-weighted (DW) echo-planar imaging (EPI) and affects apparent diffusion coefficient (ADC) calculation.This prospective study was performed on 35 adult patients and 20 volunteers. Measurements were made on a 3T scanner using a breathhold DW spin-echo EPI (SE EPI) sequence. Three protocols were used: A) eight consecutive shots at a fixed b-value of 0 seconds/mm(2) with TR = 1000 and 3000 msec; B) seven consecutive shots at b-values = 0, 1000, 750, 500, 250, 100, 0 seconds/mm(2) (in that order) with TR = 3500 msec; and C) seven consecutive shots (as in B) with TR = 1000, 1750, and 7000 msec.For protocol A, signal intensity decreased significantly from the first to second shot (P0.0001) and thereafter remained constant. For protocol B, the ADC depended on which b = 0 seconds/mm(2) image was used. Using the first b = 0 seconds/mm(2), the mean ADC was 15% higher than using the second b = 0 seconds/mm(2) (P0.0001). For protocol C, the difference between ADC using the first b = 0 seconds/mm(2) and the second b = 0 seconds/mm(2) decreased as the TR increased.The signal intensity can vary with shot number in SE EPI. For TRor = 3000 msec, steady-state is attained after one shot. Using data acquired prior to steady-state confounds the calculation of ADC values.

A novel quasi-monostatic system operating in a side-scan synthetic aperture acoustic (SAA) imaging mode is presented. This research project's objectives are to explore the military utility of outdoor continuous sound imaging of roadside foliage and target detection. The acoustic imaging method has several military relevant advantages such as being immune to RF jamming, superior spatial resolution as compared to 0.8-2.4 GHz ground penetrating radar (GPR), capable of standoff side and forward-looking scanning, and relatively low cost, weight and size when compared to GPR technologies. The prototype system's broadband 2-17 kHz LFM chirp transceiver is mounted on a manned all-terrain vehicle. Targets are positioned within the acoustic main beam at slant ranges of two to seven meters and on surfaces such as dirt, grass, gravel and weathered asphalt and with an intervening metallic chain link fence. Acoustic image reconstructions and signature plots result in means for literal interpretation and quantifiable analyses.

Liver disease/cirrhosis affects an estimated 4.5–9.5 % of the overall population (Lim and Kim, Clin Liver Dis 12:733–774, 2008). As a pain management physician, it is very important to treat patients with medications that will not cause exacerbation or progression of their liver pathology.

This letter describes the extension of signal subspace processing (SSP) to the arena of anomaly detection. In particular, we develop an SSP-based, local anomaly detector that exploits the rich information available in the multiple bands of a hyperspectral (HS) image. This SSP approach is based on signal processing considerations, and its entire formulation reduces to a straightforward (and intuitively pleasing) geometric and algebraic development. We extend the basic SSP concepts to the HS anomaly detection problem, develop an SSP HS anomaly detector, and evaluate this algorithm using multiple HS data files

This paper addresses change detection in averaged multilook synthetic aperture radar (SAR) imagery. Averaged multilook SAR images are preferable to full-aperture SAR reconstructions when the imaging algorithm is approximation-based (e.g., polar format processing) or when motion data are not accurate over a long full aperture. We examine the application of a SAR change-detection method, known as signal subspace processing, which is based on the principles of two-dimensional adaptive filtering, and we use it to recognize the addition of surface landmines to a particular area under surveillance. We describe the change-detection problem as a trinary hypothesis testing problem, and define a change signal and its normalized version to determine whether: 1) there is no change in the imaged scene; 2) a target has entered the imaged scene; or 3) a target has exited the imaged scene. A statistical analysis of the error signal is provided to show its properties and merits. Results are presented for averaged noncoherent multilook and coherent single-look X-band SAR imagery.

The results of three-dimensional (3-D) imaging of a T-72 tank using its angular azimuthal (turntable) and linear elevation synthetic aperture data at X band are presented. This is achieved using an accurate and computationally efficient wavefront (Fourier-based) reconstruction algorithm for elevation and circular (E-CSAR) data. The E-CSAR 3-D images are then used to synthesize 2-D spotlight and stripmap slant plane synthetic aperture radar (SAR) images of the target at a desired range and squint angle. For this purpose, a procedure is introduced that incorporates the spatially varying azimuthal and elevation Doppler signatures of individual reflectors on the target as well as the mean range, azimuth, and elevation of the flight path. Results using the E-CSAR images of the T-72 tank are provided.

Moving target detection and imaging results for an X band spotlight synthetic aperture radar (SAR) system that utilizes an along-track monopulse configuration for its data collection is presented. The theoretical foundation of the processing that is used on these data is based on our earlier work in which a two-dimensional signal subspace processing (adaptive filtering) method was used to calibrate the monostatic and bistatic radars of the monopulse SAR system. The blind calibration of the two channels enables the user to null the stationary scene, and detect the moving targets. Next, a measure that we call SAR ambiguity function is used to estimate the relative speed of a detected moving target. The resultant estimate is then used to image the moving target.

This correspondence addresses the problem of fusing the information content of two uncalibrated sensors. This problem arises in registering images of a scene when it is viewed via two different sensory systems, or detecting change in a scene when it is viewed at two different time points by a sensory system, or via two different sensory systems or observation channels. We are concerned with sensory systems which have not only a relative shift, scaling and rotational calibration error, but also an unknown point spread function (that is time varying for a single sensor, or different for two sensors). By modeling one image in terms of an unknown linear combination of the other image, its powers and their spatially transformed (shift, rotation and scaling) versions, a signal subspace processing is developed for fusing uncalibrated sensors. The proposed method is shown to be applicable in moving target detection (MTD) using monopulse synthetic aperture radar (SAR) with uncalibrated radars. Results are shown for video, magnetic resonance images of a human brain, moving target detector monopulse SAR, and registration of SAR images of a target obtained via two different radars or at different coordinates by the same radar for automatic target recognition (ATR).

This work addresses the problem of representing a dynamic image via its temporal spiral scan data. Two types of spiral scan data are considered: uniform density and foveal. Spatial sampling strategies for these two spiral scans are examined. A signal model is developed to interpret the temporal readouts of repeated spiral scans via two separate time variables, the slow time and fast time. This mathematical model is used to construct a method for forming the time progression of the target image. A method for increasing the repetition rate of the spiral data collection via utilizing both forward and backward spiral scans is presented. Results are provided.

This paper presents a system model and inversion for airborne synthetic aperture acoustic (SAA) imaging. The system model accurately represents the intercation of the acoustic source and the target region at near range values. Moreover, the model incorporates the fact that the relative speed of the vehicle's (transmitter/receiver) with respect to the target region is comparable to the acoustic wave propagation speed. The inversion utilizes the principle of spectral decomposition of spherical phase functions to develop a wavefront reconstruction method from SAA data. Processing issues and selection of appropriate acoustic FM-CW sources are discussed. Results are provided that exhibit the superior accuracy of the proposed SAA system model and inversion over their synthetic aperture radar (SAR) counterpart in which the vehicle's speed is assumed to be much smaller than the wave propagation speed.

This paper presents a system model and inversion for imaging moving targets using phased arrays. The system model provides a mathematical framework to represent the motion of a moving target in the beam-steering domain which is identified as the slow-time domain. The inversion provides a reconstruction of the moving targets in the spatial and velocity domains. It is shown that a randomized beam steering strategy in the slow-time domain can improve the resolution in the velocity domain. The imaging problem is also formulated for a phased array system that spotlights a target area with its transmitted beam to improve the target to clutter power ratio, and obtains beam-steered data in the receive mode for high-resolution imaging. We cite a diagnostic medical ultrasound problem due to the practical difficulties and challenges that are associated with it.

Gastroesophageal reflux disease (GERD) is a common disorder among elderly patients seeking medical care. Diagnosis and management of GERD in the older patient is a unique challenge for both the primary care provider and the gastroenterologist. Such patients may have atypical symptoms, more severe disease, and a higher rate of complications such as erosive esophagitis, Barrett esophagus, and esophageal cancer. Moreover, the elderly may be more sensitive to the morbidity and mortality of the available treatments for GERD. A careful and vigilant approach to the diagnosis, monitoring, and treatment of reflux disease in the elderly is warranted.

Noninvasive vibration diagnostic techniques are implemented to assess the mechanical condition of power circuit breakers. A diagnostic system, the prototype commercial portable diagnostic system (PCPDS) has been developed Hardware of the PCPDS includes a portable computer, and a data acquisition unit and computer communication cards. Signal processing techniques include the discrete energy statistics envelope, short-time power spectrum, timing extraction algorithm and chi-square based shape test. Decision-making is carried out via a voter program, to which individual results from the timing and shape analysis programs are passed. Statistical and empirical thresholds have been established, that classify the circuit breaker as being in normal-transitional-abnormal (green-yellow-red) condition.

The author addresses the problem of detecting and identifying stationary and moving targets with foliage penetrating UHF synthetic aperture radar (SAR). The role of a target's coherent SAR signature, which varies with the radar's frequency and aspect angle, in forming the Fourier space of the SAR signal is analyzed. The resultant relationship is the basis of an algorithm which, after extracting (digital spotlighting) the target's coherent SAR signature in the reconstruction domain, could be used to differentiate man-made structures from foliage. Methods for blind-velocity moving target indication are discussed. The main tool of the work is a signal theory based analysis of SAR signal via Fourier transform. However, the theory is at most as good as the collected SAR data. >

Two iterative algorithms were introduced for reconstructing a band-limited function from its unevenly spaced sampled data. Each iterative method introduces its own varying coefficient which is adaptively determined at each iteration. A varying coefficient for the first algorithm is adaptively determined based on minimizing the error energy of the signal at each iteration. The second method (the method of steepest descent) determines a varying coefficient based on minimizing the error energy reduction of the unevenly spaced sampled data at each iteration. We show that the reconstructed signal from these algorithms converges to the desired signal when the sampling set {t/sub n/} is a sequence such that the nonharmonic Fourier functions {exp(jwt/sub n/)} form a complete set in the signal subspace of the original signal. >

Synthetic aperture acoustic (SAA) imaging is a technique for remotely obtaining information about the location, geometry, and mechanical properties of objects based on the way they scatter incident acoustic energy. Results are presented for an experimental investigation of the use of SAA imaging to detect non-metallic cords of different sizes laid in various configurations on the ground surface in an outdoor urban environment. Interest in this application of SAA stems from the fact that non-metallic cords are not readily detectable with synthetic aperture radar (SAR) and that the SAA imaging approach represents a relatively inexpensive alternative or supplement to SAR. The measurement system is comprised of a mobile acoustic transceiver (a speaker and microphone) that broadcasts a burst chirp with a bandwidth of 2–15 kHz. The recorded signal is used to form a two-dimensional image of the distribution of acoustic scatterers within the scene. For this study, five different diameters (2–15mm) of nylon cord laid on the ground were imaged in different configurations. These measurements were made in the presence of urban ambient noise of varying levels. The goal of this study was to identify the effect of environmental noise and other parameters on detectability. The results demonstrate that non-metallic cords can be detected acoustically if the angle to the transceiver path is sufficiently small.Copyright © 2012 by ASME

This work presents a set of measurements collected with a research prototype synthetic aperture acoustic (SAA) imaging system. SAA imaging is an emerging technique that can serve as an inexpensive alternative or logical complement to synthetic aperture radar (SAR). The SAA imaging system uses an acoustic transceiver (speaker and microphone) to project acoustic radiation and record backscatter from a scene. The backscattered acoustic energy is used to generate information about the location, morphology, and mechanical properties of various objects. SAA detection has a potential advantage when compared to SAR in that non-metallic objects are not readily detectable with SAR. To demonstrate basic capability of the approach with non-metallic objects, targets are placed in a simple, featureless scene. Nylon cords of five diameters, ranging from 2 to 15 mm, and a joined pair of 3 mm fiber optic cables are placed in various configurations on flat asphalt that is free of clutter. The measurements were made using a chirp with a bandwidth of 2-15 kHz. The recorded signal is reconstructed to form a two-dimensional image of the distribution of acoustic scatterers within the scene. The goal of this study was to identify basic detectability characteristics for a range of sizes and configurations of non-metallic cord. It is shown that for sufficiently small angles relative to the transceiver path, the SAA approach creates adequate backscatter for detectability.

This paper proposes an effective anomaly detection algorithm for forward-looking ground-penetrating radar (FLGPR). The challenges in detecting explosive hazards with FLGPR are that there are multiple types of targets buried at different depths in a highly-cluttered environment. A wide array of target and clutter signatures exist, which makes classifier design difficult. Recent work in this application has focused on fusing the classifier results from multiple frequency subband images. Each sub-band classifier is trained on suites of image features, such as histogram of oriented gradients (HOG) and local binary patterns (LBP). This prior work fused the sub-band classifiers by, first, choosing the top-ranked feature at each frequency sub-band in the training data and then accumulating the sub-band results in a confidence map. We extend this idea by employing multiple kernel learning (MKL) for feature-level fusion. MKL fuses multiple sources of information and/or kernels by learning the weights of a convex combination of kernel matrices. With this method, we are able to utilize an entire suite of features for anomaly detection, not just the top-ranked feature. Using FLGPR data collected at a US Army test site, we show that classifiers trained using MKL show better explosive hazard detection capabilities than single-kernel methods.

We provide an evaluation of spectral features extracted from the signal return of a forward-looking ground penetrating radar to improve the detection performance of buried explosive hazards. The evaluations are performed on data collected at two different lanes at a government test site. The performance of the one-dimensional (1D), two-dimensional (2D) and multiple (ML) spectral features will be contrasted through lane-based cross-validation for training and testing. Additional features to characterize the spectral behaviors of the forward-looking radar return will also be examined.

Buried explosives have proven to be a challenging problem for which ground penetrating radar (GPR) has shown to be effective. This paper discusses an explosive hazard detection algorithm for forward looking GPR (FLGPR). The proposed algorithm uses the fast Fourier transform (FFT) to obtain spectral features of anomalies in the FLGPR imagery. Results show that the spectral characteristics of explosive hazards differ from that of background clutter and are useful for rejecting false alarms (FAs). A genetic algorithm (GA) is developed in order to select a subset of spectral features to produce a more generalized classifier. Furthermore, a GA-based K-Nearest Neighbor probability density estimator is employed in which targets and false alarms are used as training data to produce a two-class classifier. The experimental results of this paper use data collected by the US Army and show the effectiveness of spectrum based features in the detection of explosive hazards.

This paper proposes an effective anomaly detection algorithm for a forward-looking ground-penetrating radar(FLGPR). One challenge for threat detection using FLGPR is its high dynamic range in response to different kinds of targets and clutter objects. The application of a fixed threshold for detection in a full-band radar image often yields a large number of false alarms. We propose a method that uses both narrow-band and full-band radar processing, coupled with a classifier that uses complex-valued Gabor filter responses as the features. We then fuse the narrow-band and fullband images into a composite confidence map and detect local maxima in this map to produce candidate alarm locations. Full-band radar images provide a high degree of image resolution, while narrow-band images provide a means to detect targets which have a unique narrow-band signature. Experimental results for our improved detection techniques are demonstrated on data sets collected at a US Army test site.

Synthetic aperture image reconstruction applied to outdoor acoustic recordings is presented. Acoustic imaging is an alternate method having several military relevant advantages such as being immune to RF jamming, superior spatial resolution, capable of standoff side and forward-looking scanning, and relatively low cost, weight and size when compared to 0.5 - 3 GHz ground penetrating radar technologies. Synthetic aperture acoustic imaging is similar to synthetic aperture radar, but more akin to synthetic aperture sonar technologies owing to the nature of longitudinal or compressive wave propagation in the surrounding acoustic medium. The system's transceiver is a quasi mono-static microphone and audio speaker pair mounted on a rail 5meters in length. Received data sampling rate is 80 kHz with a 2- 15 kHz Linear Frequency Modulated (LFM) chirp, with a pulse repetition frequency (PRF) of 10 Hz and an inter-pulse period (IPP) of 50 milliseconds. Targets are positioned within the acoustic scene at slant range of two to ten meters on grass, dirt or gravel surfaces, and with and without intervening metallic chain link fencing. Acoustic image reconstruction results in means for literal interpretation and quantifiable analyses. A rudimentary technique characterizes acoustic scatter at the ground surfaces. Targets within the acoustic scene are first digitally spotlighted and further processed, providing frequency and aspect angle dependent signature information.

The use of low-frequency, ultra-wideband (UWB) radar technology to help detect concealed or buried targets has been demonstrated in the past, and could provide an important capability for combat systems on the battlefield. As part of Army's mission and technical objective, the Night Vision and Electronic Sensors Directorate, Countermine Division has designed and developed a Forward-Looking Ground-Penetrating Radar (FLGPR) with standoff capability. The forward-looking GPR is an impulse-based radar system with a bandwidth that spans between 300 – 3000 MHz. The innovative design uses commercially available, off-the shelf (COTS) components to create an effective sampling scheme of approximately 8 GHz. The design is modular, and can be scaled to provide a means for future improvement of electronic components to eventually meet the requirements of various combat systems. Early results from recent tests suggest that metallic targets buried near the surface can be detected with this radar system.

A novel outdoor synthetic aperture acoustic (SAA) system consists of a microphone and loudspeaker traveling along a 6.3-meter rail system. This is an extension from a prior indoor laboratory measurement system in which selected targets were insonified while suspended in air. Here, the loudspeaker and microphone are aimed perpendicular to their direction of travel along the rail. The area next to the rail is insonified and the microphone records the reflected acoustic signal, while the travel of the transceiver along the rail creates a synthetic aperture allowing imaging of the scene. Ground surfaces consisted of weathered asphalt and short grass. Several surface-laid objects were arranged on the ground for SAA imaging. These included rocks, concrete masonry blocks, grout covered foam blocks; foliage obscured objects and several spherical canonical targets such as a bowling ball, and plastic and metal spheres. The measured data are processed and ground targets are further analyzed for characteristics and features amenable for discrimination. This paper includes a description of the measurement system, target descriptions, synthetic aperture processing approach and preliminary findings with respect to ground surface and target characteristics.

Forward-looking ground-penetrating radar (FLGPR) has received a significant amount of attention for use in explosivehazards detection. A drawback to FLGPR is that it results in an excessive number of false detections. This paper presents our analysis of the explosive-hazards detection system tested by the U.S. Army Night Vision and Electronic Sensors Directorate (NVESD). The NVESD system combines an FLGPR with a visible-spectrum color camera. We present a target detection algorithm that uses a locally-adaptive detection scheme with spectrum-based features. The remaining FLGPR detections are then projected into the camera imagery and image-based features are collected. A one-class classifier is then used to reduce the number of false detections. We show that our proposed FLGPR target detection algorithm, coupled with our camera-based false alarm (FA) reduction method, is effective at reducing the number of FAs in test data collected at a US Army test facility.

This paper proposes an effective anomaly detection algorithm for a forward-looking ground-penetrating radar (FLGPR). One challenge for threat detection using FLGPR is its high dynamic range in response to different kinds of targets and clutter objects. The application of a fixed threshold for detection often yields a large number of false alarms. We propose a locally-adaptive detection method that adjusts the detection criteria automatically and dynamically across different spatial regions, which improves the detection of weak scattering targets. The paper also examines a spectrum-based classifier. This classifier rejects false alarms (FAs) by classifying each alarm location based on its spatial frequency-spectrum. Experimental results for the improved detection techniques are demonstrated by field data measurements from a US Army test site.

The U. S. Army Night Vision and Electronic Sensors Directorate (NVESD) recently tested an explosive-hazards detection vehicle that combines a pulsed FLGPR with a visible-spectrum color camera. Additionally, NVESD tested a human-in-the-loop multi-camera system with the same goal in mind. It contains wide field-of-view color and infrared cameras as well as zoomable narrow field-of-view versions of those modalities. Even though they are separate vehicles, having information from both systems offers great potential for information fusion. Based on previous work at the University of Missouri, we are not only able to register the UTM-based positions of the FLGPR to the color image sequences on the first system, but we can register these locations to corresponding image frames of all sensors on the human-in-the-loop platform. This paper presents our approach to first generate libraries of multi-sensor information across these platforms. Subsequently, research is performed in feature extraction and recognition algorithms based on the multi-sensor signatures. Our goal is to tailor specific algorithms to recognize and eliminate different categories of clutter and to be able to identify particular explosive hazards. We demonstrate our library creation, feature extraction and object recognition results on a large data collection at a US Army test site.

In this research, we have developed an algorithm to reduce the residual artifacts of the background clutter (that is, stationary targets) that appear in the MTI imagery that are generated by Global Signal Subspace Difference (GSSD) of the monostatic and bistatic images of an along-track monopulse synthetic aperture radar (SAR) data. We have also established the theoretical foundation for estimating the motion track and parameters of the detected moving targets. We will show the results of these algorithms on measured SAR data.

Noninvasive diagnostics, involving short-time spectra, short-time energy, and automatic timing techniques, have been applied to assess the mechanical condition of oil and puffer circuit breakers and to determine the timing of events occurring during operation. A portable instrument that employs these techniques has been developed and tested at various utilities. The capabilities of this instrument and the results of the field trials are presented. >