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.
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 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.