KEYWORDS: Synthetic aperture radar, Detection and tracking algorithms, Target detection, Land mines, Algorithm development, Unexploded object detection, Polarization, Data modeling, Data processing, Data acquisition
A team of US Army Corps of Engineers, Omaha District and Engineering and Support Center, Huntsville, JPL, Stanford Research Institute (SRI), and Montgomery Watson is currently in the process of planning and conducting the largest ever survey at the Former Buckley Field, in Colorado, by using SRI airborne, ground penetrating, SAR. The purpose of this survey is the detection of surface and subsurface Unexploded Ordnance (UXO) and in a broader sense the site characterization for identification of contaminated as well as clear areas. In preparation for such a large-scale survey, JPL has been developing advanced algorithms and a high-performance testbed for processing of massive amount of expected SAR data from this site. Two key requirements of this project are the accuracy and speed of SAR data processing. The first key feature of this testbed is a large degree of automation and maximum degree of the need for human perception in the processing to achieve an acceptable processing rate of several hundred acres per day. For accuracy UXO detection, novel algorithms have been developed and implemented. These algorithms analyze dual polarized SAR data. They are based on the correlation of HH and VV SAR data and involve a rather large set of parameters for accurate detection of UXO. For each specific site, this set of parameters can be optimized by using ground truth data. In this paper, we discuss these algorithms and their successful application for detection of surface and subsurface anti-tank mines by using a data set from Yuma Proving Ground, AZ, acquired by SRI SAR.
In this paper a novel method for solution of the acoustic wave equation is presented. This method achieves the computational efficiency of the explicit methods while also offering the excellent numerical properties of the implicit methods, i.e., the unconditional stability. We discuss the mathematical foundation of this method as well as various numerical aspects such as inclusion of the absorbing boundary conditions and efficient parallel implementations.
KEYWORDS: Algorithm development, Computer architecture, Evolutionary algorithms, Matrices, Digital signal processing, Control systems, Adaptive optics, Wavefronts, Aluminum, Computing systems
Massively parallel algorithms and architectures for real-time wavefront control of a dense adaptive optic system (SELENE) are presented. We have already shown that the computation of a near optimal control algorithm for SELENE can be reduced to the solution of a discrete Poisson equation on a regular domain. Although this represents an optimal computation, due to the large size of the system and the high sampling rate requirement, the implementation of this control algorithm poses a computationally challenging problem since it demands a sustained computational throughput of the order of 10 GFlops. We develop a novel algorithm, designated as Fast Invariant Imbedding algorithm, which offers a massive degree of parallelism with simple communication and synchronization requirements. We also discuss two massively parallel, algorithmically specialized, architectures for low-cost and optimal implementation of the Fast Invariant Imbedding algorithm.
KEYWORDS: Wavefronts, Sensors, Control systems, Adaptive optics, Monte Carlo methods, Algorithm development, Control systems design, Error analysis, Optical components, Wavefront reconstruction
This paper presents the development and analysis of a wavefront control strategy for the Space Laser Electric Energy (SELENE) power beaming system. SELENE represents a substantial departure from most conventional adaptive optics systems in that the deformable element is the segmented primary mirror and the signal that is fedback includes both the local wavefront tilt and the relative edge mismatch between adjacent segments. The major challenge in designing the wavefront control system is the large number of subapertures that must be commanded. A fast and near optimal algorithm based on the local slope and edge measurements is defined for this system.
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