LeRoy A. Gorham

SAR image formation toolbox for MATLAB

While many synthetic aperture radar (SAR) image formation techniques exist, two of the most intuitive methods for implementation by SAR novices are the matched filter and backprojection algorithms. The matched filter and (non-optimized) backprojection algorithms are undeniably computationally complex. However, the backprojection algorithm may be successfully employed for many SAR research endeavors not involving considerably large data sets and not requiring time-critical image formation. Execution of both image reconstruction algorithms in MATLAB is explicitly addressed. In particular, a manipulation of the backprojection imaging equations is supplied to show how common MATLAB functions, ifft and interp1, may be used for straight-forward SAR image formation. In addition, limits for scene size and pixel spacing are derived to aid in the selection of an appropriate imaging grid to avoid aliasing. Example SAR images generated though use of the backprojection algorithm are provided given four publicly available SAR datasets. Finally, MATLAB code for SAR image reconstruction using the matched filter and backprojection algorithms is provided.

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.

Ground moving target detection and tracking based on generalized SAR processing and change detection

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.

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.

Implementation and analysis of a fast backprojection algorithm

The convolution backprojection algorithm is an accurate synthetic aperture radar imaging technique, but it has seen limited use in the radar community due to its high computational costs. Therefore, significant research has been conducted for a fast backprojection algorithm, which surrenders some image quality for increased computational efficiency. This paper describes an implementation of both a standard convolution backprojection algorithm and a fast backprojection algorithm optimized for use on a Linux cluster and a field-programmable gate array (FPGA) based processing system. The performance of the different implementations is compared using synthetic ideal point targets and the SPIE XPatch Backhoe dataset.

Scene size limits for polar format algorithm

Synthetic aperture radar (SAR) is a form of remote sensing where coherent radar echoes transmitted from a moving platform are processed to form an image of a scene, usually on the ground. There are several algorithms that have been developed with varying levels of complexity and accuracy. In applications with large scene size requirements, the choice of image formation algorithm is important. Exact imaging algorithms like the back-projection algorithm (BPA) can form large images without errors, but they are computationally expensive. Another well-known algorithm is the polar format algorithm (PFA), which is significantly faster than BPA, but it uses approximations that cause image errors in large scenes. In this paper, we evaluate the scene size limitations of the PFA in terms of image defocus. This is caused by residual quadratic phase errors that arise due to approximations in the algorithm. We derive this residual quadratic phase error using a Taylor series expansion in the slow time dimension. Then, we derive simplified expressions for image defocus for two flight paths: circular and linear. We also use the Taylor series expansion to derive accurate corrections for image distortion caused by PFA. These distortion corrections are used in conjunction with the residual quadratic phase errors to derive accurate scene size limitations that are notably different from the circular regions of focus determined in earlier works.

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.

Radar signals dismount data production

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.