There is a history and understanding of exploiting moving targets within ground moving target indicator (GMTI) data, including methods for modeling performance. However, many assumptions valid for GMTI processing are invalid for synthetic aperture radar (SAR) data. For example, traditional GMTI processing assumes targets are exo-clutter and a system that uses a GMTI waveform, i.e. low bandwidth (BW) and low pulse repetition frequency (PRF). Conversely, SAR imagery is typically formed to focus data at zero Doppler and requires high BW and high PRF. Therefore, many of the techniques used in performance estimation of GMTI systems are not valid for SAR data. However, as demonstrated by papers in the recent literature,1-11 there is interest in exploiting moving targets within SAR data. The techniques employed vary widely, including filter banks to form images at multiple Dopplers, performing smear detection, and attempting to address the issue through waveform design. The above work validates the need for moving target exploitation in SAR data, but it does not represent a theory allowing for the prediction or bounding of performance. This work develops an approach to estimate and/or bound performance for moving target exploitation specific to SAR data. Synthetic SAR data is generated across a range of sensor, environment, and target parameters to test the exploitation algorithms under specific conditions. This provides a design tool allowing radar systems to be tuned for specific moving target exploitation applications. In summary, we derive a set of rules that bound the performance of specific moving target exploitation algorithms under variable operating conditions.
Synthetic aperture radar (SAR) imaging is often used to image an area using airborne platforms that generate a large aperture by virtue of the platform motion. Large apertures generate a large synthetic array providing fine cross-range resolution, and together with wide bandwidth waveforms that provide high range resolution, fine resolution images can be generated. SAR algorithms make use of coherent phase compensation from various pulses for focusing and the technique works exceedingly well for scenes containing stationary scattering centers. When moving targets are present, their images are smeared and shifted due to the motion, and to take advantage of this shift, nearby receiver plates are used to form multiple SAR images and together with along track interferometry (ATI), it generates a phase factor that can be used to detect moving target presence.
This paper examines the distribution of the phase variable used in ATI for zero mean Complex Gaussian clutter/target data, and uses the results to address the target in clutter problem as a hypothesis testing problem to compute the probability of detection/false alarm as a function of target to clutter ratio and its velocity.
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.
Advanced spread spectrum linear frequency modulated (LFM) waveforms are being developed for advanced capability synthetic aperture radar (SAR) and ground moving target indication (GMTI) applications. We have demonstrated by analysis and simulation the feasibility of these new type waveforms and are now in the process of implementing them in hardware. The basic approach is to combine a traditional LFM radar waveform with a direct sequence spread spectrum (DSSS) waveform, and then on receive to de-spread the return and capture the resultant LFM return for traditional matched filter processing and enhanced SAR and GMTI. We show the analysis, simulation and some preliminary hardware results.
Wideband radar waveforms that employ spread-spectrum techniques were investigated and experimentally tested. The waveforms combine bi-phase coding with a traditional LFM chirp and are applicable to joint SAR-GMTI processing. After de-spreading, the received signals can be processed to support simultaneous GMTI and high resolution SAR imaging missions by airborne radars. The spread spectrum coding techniques can provide nearly orthogonal waveforms and offer enhanced operations in some environments by distributing the transmitted energy over a large instantaneous bandwidth. The LFM component offers the desired Doppler tolerance. In this paper, the waveforms are formulated and a shift-register approach for de-spreading the received signals is described. Hardware loop-back testing has shown the feasibility of using these waveforms in experimental radar test bed.
This document describes a challenge problem whose scope is two-fold. The first aspect is to develop SAR CCD
algorithms that are applicable for X-band SAR imagery collected in an urban environment. The second aspect relates to
effective data compression of these complex SAR images, where quality SAR CCD is the metric of performance.
A set of X-band SAR imagery is being provided to support this development. To focus research onto specific areas of
interest to AFRL, a number of challenge problems are defined.
The data provided is complex SAR imagery from an AFRL airborne X-band SAR sensor. Some key features of this data
set are: 10 repeat passes, single phase center, and single polarization (HH). In the scene observed, there are multiple
buildings, vehicles, and trees. Note that the imagery has been coherently aligned to a single reference.
A major area of focus for the Air Force is sensor performance in urban environments.
Aircraft with multiple sensor modalities, such as Synthetic Aperture RADAR (SAR),
Infrared (IR), and Electro-Optics (EO), are essential for intelligence, surveillance, and
reconnaissance (ISR) of current and future urban battlefields. Although applications
exist for visualization of these types of imagery, they usually require at least a laptop
computer and internet connection. Field operatives need to be able to access
georeferenced information about imagery as part of a Geographic Information System
(GIS) on mobile devices. The iPod/iPhone has a 640x480 resolution multi-touch display,
making it an excellent device for interacting with georeferenced imagery. We created an
iPhone application that loads SAR imagery and allows the user to interact with it. The
user multi-touch interface provides pan and zoom capabilities as well as options to
change parameters relating to the query. We describe how operatives in the field can use
this application to investigate SAR and GIS related problems on the iPhone mobile
device, which otherwise would require a computer and Internet connection.
This document describes a challenge problem whose scope is the detection, geolocation, tracking
and ID of moving vehicles from a set of X-band SAR data collected in an urban environment. The
purpose of releasing this Gotcha GMTI Data Set is to provide the community with X-band SAR data
that supports the development of new algorithms for SAR-based GMTI. To focus research onto
specific areas of interest to AFRL, a number of challenge problems are defined.
The data set provided is phase history from an AFRL airborne X-band SAR sensor. Some key
features of this data set are two-pass, three phase center, one-foot range resolution, and one
polarization (HH). In the scene observed, multiple vehicles are driving on roads near buildings.
Ground truth is provided for one of the vehicles.
It is believed that the LMS algorithm can be used to form a two dimensional image from radar return data. A version of
the LMS algorithm was written to form SAR images from radar phase history data. Images were formed from fifty sets
of twenty synthetically generated random points. The signal-to-noise ratio (SNR) was measured for each image and
showed an average of 30 dB. A more complex synthetic scene was also formed from a black and white JPEG image.
This image showed excellent qualitative results. Actual radar range data was collected from a set of vertical pins and
from a model car painted with conductive paint. The data was from a full 360-degree aperture. The resulting images
were of high quality; the vertical pins acted as pure point sources and indicated that the image had a resolution of 4.5
mm, which agrees with theory.
Proc. SPIE. 6568, Algorithms for Synthetic Aperture Radar Imagery XIV
KEYWORDS: Logic, Synthetic aperture radar, Image processing, Computing systems, Field programmable gate arrays, X band, Data processing, Signal processing, Automatic target recognition, Algorithm development
A fundamental issue with synthetic aperture radar (SAR) application development is data processing and exploitation in
real-time or near real-time. The power of high performance computing (HPC) clusters, FPGA, and the IBM Cell
processor presents new algorithm development possibilities that have not been fully leveraged. In this paper, we will
illustrate the capability of SAR data exploitation which was impractical over the last decade due to computing
limitations. We can envision that SAR imagery encompassing city size coverage at extremely high levels of fidelity
could be processed at near-real time using the above technologies to empower the warfighter with access to critical
information for the war on terror, homeland defense, as well as urban warfare.
This paper describes a challenge problem whose scope is the 2D/3D imaging of stationary targets from a volumetric data
set of X-band Synthetic Aperture Radar (SAR) data collected in an urban environment. The data for this problem was
collected at a scene consisting of numerous civilian vehicles and calibration targets. The radar operated in circular SAR
mode and completed 8 circular flight paths around the scene with varying altitudes. Data consists of phase history data,
auxiliary data, processing algorithms, processed images, as well as ground truth data. Interest is focused on mitigating
the large side lobes in the point spread function. Due to the sparse nature of the elevation aperture, traditional imaging
techniques introduce excessive artifacts in the processed images. Further interests include the formation of highresolution
3D SAR images with single pass data and feature extraction for 3D SAR automatic target recognition
applications. The purpose of releasing the Gotcha Volumetric SAR Data Set is to provide the community with X-band
SAR data that supports the development of new algorithms for high-resolution 2D/3D imaging.
Single-channel synthetic aperture radar (SAR) can provide high quality, focused images of moving targets by utilizing
advanced SAR-GMTI techniques that focus all constant velocity targets into a three-dimensional space indexed by
range, cross-range and cross-range velocity. However, an inherent geolocation ambiguity exists in that multiple, distinct
moving targets may posses identical range versus time responses relative to a constant velocity collection platform.
Although these targets are uniquely located within a four-dimensional space (x-position, y-position, x-velocity, and y-velocity),
their responses are focused and mapped to the same three-dimensional position in the SAR-GMTI image cube.
Previous research has shown that circular SAR (CSAR) collection geometry is one way to break this ambiguity and
creates a four-dimensional detection space. This research determines the target resolution available in the detection
space as a function of different collection parameters. A metric is introduced to relate the resolvability of multiple target
responses for various parametric combinations, i.e., changes in key collection parameters such as integration time, slant
range, look angle, and carrier frequency.
For synthetic aperture radar (SAR) systems utilizing a circular aperture for target recognition, it is important to know
how a target's point spread function (PSF) behaves as a function of various radar functional parameters and target
positional changes that may occur during data collection. The purpose of this research is characterizing the three
dimensional (3D) point spread function (3D PSF) behavior of a radially displaced point scatterer for circular synthetic
aperture radar (CSAR). For an automatic target recognition (ATR) systems requiring target identification with a high
degree of confidence, CSAR processing represents a viable alternative given it can produce images with resolution less
than a wavelength. With very large CSAR apertures (90°r; or more) three dimensional imaging is possible with a single
phase center and a single pass. Using a backprojection image formation process, point target PSF responses are
generated at various target locations at a given radar bandwidth, depression angle and full 360°r; CSAR apertures.
Consistent with previous studies, the 3D PSF for a point target located at the image center is cone shaped and serves as
the basis for comparing and characterizing the 3D PSFs for radially displaced scatterers. For radially displaced point
target, simulated results show 3D PSF response is asymmetric and tends to become an elliptic shape.
Recent technology developments in digital radio, low-cost inertial navigation systems and unmanned air vehicle design are converging to enable and make practical several new radar sensing modes such as simultaneous SAR/GMTI from persistent staring-mode radar, 3D SAR from a single-pass, single phase center radar and wide-angle radar tracking of dismounts. One of the challenges for algorithm developers is a lack of high-quality target and clutter signature data from the new radar modes. AFRL's Sensor Directorate and SET Corporation are developing a compact, low-cost wide-angle radar test bed capable of simulating a variety of radar modes, including 3D SAR, SAR/GMTI from staring-mode radar and ultra-fine resolution range-Doppler. We provide an overview of the wide-angle radar test bed architecture, its modular design and our implementation approach. We then describe several non-conventional wide-angle radar sensor modes and outline a corresponding series of test bed data collection experiments that could be used to support the development of new tracking and recognition algorithms.
SAR-MTI is a generalization of SAR processing and can work with only a single-phase center. SAR-MTI requires formation of a stack of SAR images assuming different sensor ground speeds. Each image will capture a different set of target velocities, and the complete set of images will focus all target speeds less than a desired maximum speed regardless of direction and target location. SAR-MTI has no minimum detectable velocity. SAR-GMTI will have higher resolution than standard GMTI because the SAR based processing allows longer coherent processing intervals (CPI). A realistic dense moving target scenario was simulated using SAR-GMTI detection and standard GMTI detection was simulated for this scenario and the frequency of unresolved targets was compared. SAR-GMTI suffered many fewer unresolvable target pairs because of its smaller resolution cell and because it inherently detects in a 3 dimensional space vs. a two dimensional space. Furthermore SAR-GMTI does not have a clutter notch, which eliminated about 15% of the moving targets for standard GMTI.
It is critical in urban environments to not only track cars and tanks; but also individuals. Tracking dismounts, whereby an individual exits a car, can be done using conventional Electro-Optical (full color) or Infrared (thermal) cameras. However, EO/IR systems are subject to weather and line-of-sight conditions (i.e. person blocked by cloud) as well are degraded for long ranges. In this study, we pursue the use of radar images for dismount tracking. Radar dismount tracking will not entail the same robust features for person identification as EO systems; however, by being able to maintain track in all-weather conditions would afford friendly forces a location of all moving individuals. We show, using a feature-based tracker, that dismount detection, tracking, and potential intent, is possible. Radio Frequency (RF) tracking of dismounts is a relatively new concept because the data has not been available. By forming a data set based on the POSERTM program, and post-processing the data, we are interested in exploring the possibility of RF dismount tracking. In this paper, we (1) explore the generation of RF dismount data, (2) apply feature-based tracking algorithm to locate the moving target, and (3) assess the performance.
This paper examines the theory, application, and results of using single-channel synthetic aperture radar (SAR) data with Moving Reference Processing (MRP) to focus and geolocate moving targets. Moving targets within a standard SAR imaging scene are defocused, displaced, or completely missing in the final image. Building on previous research at AFRL, the SAR-MRP method focuses and geolocates moving targets by reprocessing the SAR data to focus the movers rather than the stationary clutter. SAR change detection is used so that target detection and focusing is performed more robustly. In the cases where moving target returns possess the same range versus slow-time histories, a geolocation ambiguity results. This ambiguity can be resolved in a number of ways. This paper concludes by applying the SAR-MRP method to high-frequency radar measurements from persistent continuous-dwell SAR observations of a moving target.
It has recently become apparent that dismount tracking from non-EO based sources will have a
large positive impact on urban operations. EO / camera imaging is subject to line of site and
weather conditions which makes it a non-robust source for dismount tracking. Other sensors
exist (e.g. radar) to track dismount targets; however, little radar dismount data exists. This paper
examines the capability to generate synthetic and measured dismount data sets for radar
frequency (RF) processing. For synthetic data, we used the PoserTM program to generate 500
facet models of human dismount walking. Then we used these facet models with Xpatch to
generate synthetic wideband radar data. For measured dismount data, we used a multimode (X-Band
and Ku-Band) radar system to collect RF data of volunteer human (dismount) targets.
ViSUAl-D (VIsual Sar Using ALl Dimensions), a 2004 DARPA/IXO seedling effort, is developing a capability for reliable high confidence ID from standoff ranges. Recent conflicts have demonstrated that the warfighter would greatly benefit from the ability to ID targets beyond visual and electro-optical ranges. Forming optical-quality SAR images while exploiting full polarization, wide angles, and large bandwidth would be key evidence such a capability is achievable. Using data generated by the Xpatch EM scattering code, ViSUAl-D investigates all degrees of freedom available to the radar designer, including 6 GHz bandwidth, full polarization and angle sampling over 2π steradians (upper hemisphere), in order to produce a "literal" image or representation of the target.
This effort includes the generation of a "Gold Standard" image that can be produced at X-band utilizing all available target data. This "Gold Standard" image of the backhoe will serve as a test bed for future more relevant military targets and their image development. The seedling team produced a public release data which was released at the 2004 SPIE conference, as well as a 3D "Gold Standard" backhoe image using a 3D image formation algorithm. This paper describes the full backhoe data set, the image formation algorithm, the visualization process and the resulting image.
A unified way of detecting and tracking moving targets with a SAR radar called SAR-MTI is presented. SAR-MTI differs from STAP or DPCA in that it is a generalization of SAR processing and can work with only a single phase center. SAR-MTI requires formation of a series of images assuming different sensor ground speeds, from vs-vtmax to vs+vtmax, where vs is the actual sensor ground speed and vtmax is the maximum target speed of interest. Each image will capture a different set of target velocities, and the complete set of images will focus all target speeds less than a desired maximum speed regardless of direction and target location. Thus the 2-dimensional SAR image is generalized to a 3-dimensional cube or stack of images. All linear moving targets less than the desired speed will be focused somewhere in the cube. The third dimension represents the along track velocity of the mover which is a piece of information not available to standard airborne MTI. A mover will remain focused at the same place within the cube as long as the motion of the mover and the sensor remain linear. Because stationary targets also focus within the detection cube, move-stop-move targets are handled smoothly and without changing waveforms or modes. Another result of this fact is that SAR-MTI has no minimum detectable velocity.
SAR-MTI has an inherent ambiguity because the four-dimensions of target parameters (two dimensions in both velocity and position) are mapped into a three-dimensional detection space. This ambiguity is characterized and methods for resolving the ambiguity for geolocation are discussed. The point spread function in the detection cube is also described.
Development of phase history calibration techniques is important for improving Synthetic Aperture Radar (SAR) scene modeling capabilities. Image data of complex scene settings is used for clutter database construction, and the resulting databases are used in conjunction with synthetic radar predictions of complex targets to predict synthetic SAR imagery. The current method of trihedral calibration is typically performed after image formation, using a ratiometric technique, which is highly dependent on calibration target position and orientation and ground truth accuracy. As part of a recent SAR research data collection, measurements were made on a calibration-grade, 6-meter diameter top hat in both a homogeneous scene and with controlled obscuration and layover conditions. This paper will discuss phase-history calibration target design and scenario design to support obscuration and layover studies.