Steven Scarborough

A challenge problem for 2D/3D imaging of targets from a volumetric data set in an urban environment

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.

A challenge problem for SAR-based GMTI in urban environments

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.

A Challenge Problem for SAR Change Detection and Data Compression.

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.

Wide angle SAR data for target discrimination research

An airborne circular synthetic aperture radar system captured data for a 5 km diameter area over 31 orbits. For this challenge problem, the phase history for 56 targets was extracted from the larger data set and placed on a DVD for public release. The targets include 33 civilian vehicles of which many are repeated models, facilitating training and classification experiments. The remaining targets include an open area and 22 reflectors for scattering and calibration research. The circular synthetic aperture radar provides 360 degrees of azimuth around each target. For increased elevation content, the collection contains two nine-orbit volumetric series, where the sensor reduces altitude between each orbit. Researchers are challenged to further the art of focusing, 3D imaging, and target discrimination for circular synthetic aperture radar.

Synthetic Aperture Radar moving target indication processing of along-track monopulse nonlinear gotcha data

This paper is concerned with imaging and moving target detection using a Synthetic Aperture Radar (SAR) platform that is known as Gotcha. The SAR platform can interrogate a scene using an imperfect circular trajectory; we refer to this as nonlinear SAR data collection. This collection can make monostatic and quasi-monostatic measurements in the along-track domain. We present subaperture-based wavefront reconstruction algorithms for motion compensation and imaging from this nonlinear SAR database. We also discuss adaptive filtering algorithms to construct MTI imagery from the two receiver channels of the system. Results will be provided.

Detection and tracking of moving vehicles with Gotcha radar systems

The authors have defined an approach for detecting and tracking moving vehicles with Gotcha radar systems. Our approach exploits tracker feedback to address challenges for detection, false alarm mitigation, geolocation, and tracking in an urban surveillance environment. The authors presented a mathematical framework for processing multichannel SAR data in order to mitigate the combined effects of moving target defocus and strong clutter interference. The algorithm uses MRP adaptively in a STAP framework to focus up moving vehicles and enhance signal to clutter ratios. Using simulated data, we illustrated SAR defocus of an accelerating point scatterer and mitigation of the defocus using MRP. For this simulated example, we also presented the potential improvements to SINR loss using our adaptive processing compared to more standard STAP approaches.

Improving knowledge-aided STAP performance using past CPI data [radar signal processing]

A technique for incorporating past coherent processing interval (CPI) radar data into knowledge-aided space-time adaptive processing (KASTAP) is described. The technique forms Earth-based clutter reflectivity maps to provide improved knowledge of clutter statistics in nonhomogeneous terrain environments. The maps are utilized to calculate predicted clutter covariance matrices as a function of range. Using a data set provided under the DARPA knowledge-aided sensor signal processing and expert reasoning (KASSPER) program, predicted clutter statistics are compared to measured statistics to verify the accuracy of the approach. Robust STAP weight vectors are calculated using a technique that combines covariance tapering, adaptive estimation of gain and phase corrections, knowledge-aided pre-whitening, and eigenvalue rescaling. Several performance metrics are calculated, including signal-to-interference plus noise (SINR) loss, target detections and false alarms, receiver operating characteristic (ROC) curves, and tracking performance. The results show a significant benefit to using knowledge-aided processing based on multiple CPI clutter reflectivity maps.

Target geolocation in Gotcha data using cross-channel interferometry

This paper discusses application of a cross-channel interferogram based technique for target geolocation to data from the Gotcha sensor. A modified SAR/ATI/STAP geolocation method is used to compensate for the inability to perform STAP well using the available Gotcha data due to limitations in spatial degrees of freedom. SAR/ATI geolocation is illustrated with three examples from the measurements for: two known targets with truth data, and an unknown target. The first known target is geolocated with a 15.6m accuracy; the unknown target is geolocated with reasonable accuracy for 8 out of 11 detections; and the second known target is geolocated with an average error of 13.18m for 10 consecutive CPIs.

Staring RF signal processing challenges

Traditionally, distinct radar modes have been employed to accomplish specific tasks such as imaging an area of interest, or detecting and tracking moving targets. Staring circular synthetic aperture radar (S-CSAR) provides unique opportunities for exploitation of radio frequency (RF) data collected over a large ground spot. The same phase history may be processed in different manners to generate simultaneous S-CSAR products such as 2-D Video SAR, coherent and non-coherent change detection (CCD and NCD), and ground moving target indication (GMTI). Advanced signal processing techniques can take advantage of the S-CSAR geometry to produce 3-D scene reconstructions. The ability to transmit, record and process large volumes of S-CSAR data, to create high fidelity exploitation products, in real-time, poses significant challenges. This paper addresses several open problems in this research area.

Target geolocation in Gotcha data using panoramic processing

Any signal processing methodology when blindly applied to realistic data sets generates a significant number of false targets along with estimates for the true moving targets. In an effort to isolate the true movers from the false targets, a new approach exploiting spatio-temporal connectivity in addition to signal processing algorithms involving imaging and interferometry is proposed here to geolocate the movers in a measured data set.