In this paper, we present the results of a modeling study to examine the interference effect of microDopplers caused by offshore wind farms on airborne sensors operating in the synthetic aperture radar (SAR) and ground moving target indicator (GMTI) modes. The modeling is carried out by generating CAD instantiations of the dynamic wind turbine and using the high-frequency electromagnetic code Xpatch to perform the scattering calculations. Artifacts in the resulting SAR and GMTI signatures are evaluated for interference with tracking of boats in coastal waters. Results of signal filtering algorithms to reduce the dynamic turbine clutter in both SAR images and GMTI displays are presented.
In this paper we present an algorithm for target validation using 3-D scattering features. Building a high fidelity
3-D CAD model is a key step in the target validation process. 3-D scattering features were introduced previously  to
capture the spatial and angular scattering properties of a target. The 3-D scattering feature set for a target is obtained by
using the 3-D scattering centers predicted from the shooting and bouncing ray technique, and establishing a
correspondence between the scattering centers and their associated angular visibility. A 3-D scattering feature can be
interpreted to be a matched filter for a target, since the radar data projected onto the feature are matched to the spatial
and angular scattering behavior of the target. Furthermore, the 3-D scattering features can be tied back to the target
geometries using the trace-back information computed during the extraction process. By projecting the measured radar
data onto a set of 3-D scattering features and examining the associated correlations and trace-back information, the
quality of the 3-D target CAD model used for synthetic signature modeling can be quantified. The correlation and traceback
information can point to regions of a target that differ from the 3-D CAD model. Results for the canonical Slicy
target using the algorithm are presented.
The electromagnetic scattered field from an electrically large target can often be well modeled as if it is emanating
from a discrete set of scattering centers (see Fig. 1). In the scattering center extraction tool we developed previously based on the shooting and bouncing ray technique, no correspondence is maintained amongst the 3D scattering center extracted at adjacent
angles. In this paper we present a multi-dimensional clustering algorithm to track the angular and spatial behaviors of 3D
scattering centers and group them into features. The extracted features for the Slicy and backhoe targets are presented. We also
describe two metrics for measuring the angular persistence and spatial mobility of the 3D scattering centers that make up these
features in order to gather insights into target physics and feature stability. We find that features that are most persistent are also
the most mobile and discuss implications for optimal SAR imaging.
Proc. SPIE. 5808, Algorithms for Synthetic Aperture Radar Imagery XII
KEYWORDS: Radar, 3D acquisition, 3D image reconstruction, Detection and tracking algorithms, Scattering, Synthetic aperture radar, Image acquisition, 3D modeling, Electromagnetic scattering, 3D image processing
The performance of ATR systems can potentially be improved by using three-dimensional (3-D) SAR images instead of the traditional two-dimensional SAR images or one-dimensional range profiles. 3-D SAR image formation of targets from radar backscattered data collected on wide angle, sparse apertures has been identified by AFRL as fundamental to building an object detection and recognition capability. A set of data has been released as a challenge problem. This paper describes a technique based on the concept of 3-D target grids aimed at the formation of 3-D SAR images of targets from sparse aperture data. The 3-D target grids capture the 3-D spatial and angular scattering properties of the target and serve as matched filters for SAR formation. The results of 3-D SAR formation using the backhoe public release data are presented.
This paper describes the results of a multi-baseline IFSAR study using a shooting and bouncing ray (SBR) based IFSAR simulator. The SBR technique has been used in the past for 2-D SAR and IFSAR simulations. This paper extends on those approaches for modeling multi-baseline IFSAR images. IFSAR gives the height estimate for a target and hence leads to a 3-D image of the target. The 3-D reconstruction is dependent on the choice of IFSAR sensor parameters. We present a tradeoff study the sensor resolution versus the number of baselines using the SBR based simulator.
In this paper, ISAR images generated from measured data are compared to those from computer simulation in order to evaluate the effectiveness of ISAR-based target identification. Three sets of images are generated including: (1) motion compensated images from measured data using a joint time-frequency technique, (2) reference images from measured data and GPS-derived aircraft attitude data, and (3) synthetic images predicted by Xpatch. Visual examination and correlation analysis are undertaken to compare the three sets of images. In addition, two problem areas including JEM line corruption of the measured images and 3D rotation of the target are identified.
Proc. SPIE. 3721, Algorithms for Synthetic Aperture Radar Imagery VI
KEYWORDS: 3D acquisition, 3D image reconstruction, Detection and tracking algorithms, Scattering, Image processing, Electroluminescence, 3D modeling, Electromagnetic scattering, Solid modeling, 3D image processing
We present a technique to extract the three-dimensional (3-D) bistatic scattering center model of a target at microwave frequencies from its CAD model. The method is based on the shooting and bouncing ray (SBR) technique and is an extension of our previous work on extracting the monostatic 3-D scattering center model of complex targets. Using SBR, we first generate the bistatic 3-D radar image of the target based on a one-look inverse synthetic aperture radar (ISAR) algorithm. Next, we use the image processing algorithm CLEAN to extract the 3-D position and strength of the scattering centers from the bistatic radar image. We test the algorithm by extracting bistatic 3-D scattering centers from several test targets and reconstructing bistatic signatures (RCS, range profile, ISAR imagery) using the bistatic scattering centers.