This article presents different strategies for generating very large sets of SAR phase history and imagery for target recognition studies using the open-use Raider Tracer simulation tool. Previous data domes, based on Visual D, produced numerous data sets for ground targets above a flat surface, but each target had a single orientation. Here, the experiment specifies different target types, each above a ground plane, but with arbitrary pose, yaw, and pitch. The customized data set poses challenges to load balancing and file input/output synchronization for a limited cpu hour budget. Strategies are presented to complete each image within a minimal time, and to generate the complete experiment set within a desired time.
An algorithm is presented for synthesizing mathematical models of terrain elevation and re ectivity from digital elevation terrain data (DTED) and national land cover data (NLCD). Assuming the DTED and NLCD have spatial intersection, it is straightforward to interpolate each set individually to a common set of coordinates in the intersection. However, DTED is continuous and NLCD is not typically which results in different and sometimes contrasting sampling requirements of the intersecting region. This study evaluates different similarity measures used to assess the quality of re-sampling DTED and NLCD data for the purpose of building elevation and reflectivity profiles for physical optics calculation of site-specific radar clutter. Examples of the algorithm are presented for clutter scene generation with the Raider Tracer prediction tool.
This paper investigates the performance of single-channel SAR-GMTI systems in the focusing and detection
of translating ground targets moving in the presence of a clutter background. Specifically, focusing and detection
performance is investigated by applying the Moving Grid Processing (MGP) focusing technique to a scene
containing an accelerating target moving in the presence of both uniform and correlated K-distributed clutter
backgrounds. The increase in detection sensitivity resulting from the focusing operation is found to result from
two separable effects, target focusing and clutter defocusing. While the detection sensitivity gain due to target
focusing is common for both clutter types, the gain due to clutter defocusing is found to be significantly greater
for textured clutter than for uniform clutter, by approximately 5 to 6 dB in the simulated scenario under consideration.
This paper concludes with a discussion of the phenomenological causes for this difference and implications
of this finding for single channel SAR-GMTI systems operating in heterogeneous clutter environments.
The spectrum parted linked image test (SPLIT) algorithm was experimentally shown to estimate frequency-dependency
of dominant scattering centers through sub-band analysis. Based on its demonstrated potential for classifying canonical
scatterers, a theoretical model of the SPLIT algorithm is presented in this paper. Terms are defined, procedures are
detailed, and a metric for total least squares model fitting is developed. In addition, the paper addresses multiple
observations, measures of confidence, sidelobe interference and sensitivity to bandwidth and noise. Finally, it is
described how the one-dimensional (1D) SPLIT algorithm can be extended for use with 2D and 3D imaging.
This paper investigates the relationship between a ground moving target's kinematic state and its SAR image.
While effects such as cross-range offset, defocus, and smearing appear well understood, their derivations in the
literature typically employ simplifications of the radar/target geometry and assume point scattering targets.
This study adopts a geometrical model for understanding target motion effects in SAR imagery, termed the
target migration path, and focuses on experimental verification of predicted motion effects using both simulated
and empirical datasets based on the Gotcha GMTI challenge dataset. Specifically, moving target imagery is
generated from three data sources: first, simulated phase history for a moving point target; second, simulated
phase history for a moving vehicle derived from a simulated Mazda MPV X-band signature; and third, empirical
phase history from the Gotcha GMTI challenge dataset. Both simulated target trajectories match the truth GPS
target position history from the Gotcha GMTI challenge dataset, allowing direct comparison between all three
imagery sets and the predicted target migration path. This paper concludes with a discussion of the parallels
between the target migration path and the measurement model within a Kalman filtering framework, followed
This paper presents an algorithm for estimating the frequency dependence of scattering center amplitudes. The
spectrum parted linked image test (SPLIT) algorithm compares the amplitude peaks in two images formed by
splitting the radar signal bandwidth into two parts. The theoretical basis for the algorithm lies in the Geometrical
Theory of Diffraction. This theoretical basis, a description of the algorithm, and experimental results are provided.
The authors recommend the use of this algorithm for high frequency (> 5 GHz), wide-band (> 1 GHz) applications
involving target detection and recognition.
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 (<i>x</i>-position, <i>y</i>-position, <i>x</i>-velocity, and <i>y</i>-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.