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  1. MREG V1.1 is the sixth generation SAR image registration algorithm developed by the Signal Processing&Technology Department for Synthetic Aperture Radar applications. Like its predecessor algorithm REGI, it employs a powerful iterative multi-scale paradigm to achieve the competing goals of sub-pixel registration accuracy and the ability to handle large initial offsets. Since it is not model based, it allows for high fidelity tracking of spatially varying terrain-induced misregistration. Since it does not rely on image domain phase, it is equally adept at coherent and noncoherent image registration. This document provides a brief history of the registration processors developed by Dept. 5962more » leading up to MREG V1.1, a full description of the signal processing steps involved in the algorithm, and a user's manual with application specific recommendations for CCD, TwoColor MultiView, and SAR stereoscopy.« less
  2. Typical synthetic aperture RADAR (SAR) imaging employs a co-located RADAR transmitter and receiver. Bistatic SAR imaging separates the transmitter and receiver locations. A bistatic SAR configuration allows for the transmitter and receiver(s) to be in a variety of geometric alignments. Sandia National Laboratories (SNL) / New Mexico proposed the deployment of a ground-based RADAR receiver. This RADAR receiver was coupled with the capability of digitizing and recording the signal collected. SNL proposed the possibility of creating an image of targets the illuminating SAR observes. This document describes the developed hardware, software, bistatic SAR configuration, and its deployment to test themore » concept of a ground-based bistatic SAR. In the proof-of-concept experiments herein, the RADAR transmitter will be a commercial SAR satellite and the RADAR receiver will be deployed at ground level, observing and capturing RADAR ground/targets illuminated by the satellite system.« less
  3. IFP V4.0 is the fourth generation of an extraordinarily powerful and flexible image formation processor for spotlight mode synthetic aperture radar. It has been successfully utilized in processing phase histories from numerous radars and has been instrumental in the development of many new capabilities for spotlight mode SAR. This document provides a brief history of the development of IFP, a full exposition of the signal processing steps involved, and a short user's manual for the software implementing this latest iteration.
  4. In this paper we have shown how the direction cosine method of stripmap-mode IFSAR maybe modified for use in the spotlight-mode case. Spotlight-mode IFSAR geometry dictates a common aperture phase center, velocity vector, and baseline vector for every pixel in an image. Angle with respect to the velocity vector is the same for every pixel in a given column and can be computed from the column index, the Doppler of the motion compensation point and the Doppler column sample spacing used in image formation. With these modifications, the direction cosines and length of the line of sight vector to everymore » scatterer in the scene may be computed directly from the raw radar measurements of range, Doppler, and interferometric phase.« less
  5. No abstract prepared.
  6. Spatially Interpolated Nonlinear Anodization in Synthetic Aperture Original formulation of spatially variant anodization for complex synthetic aperture radar (SAR) imagery oversampled at twice the Nyquist rate (2.OX). Here we report a spatially interpolating, noninteger-oversampled SVA sidelobe. The pixel's apparent IPR location is assessed by comparing its value to the sum of its value plus weighted comparable for exact interpolation. However, exact interpolation implies an ideal sine interpolator3 and large components may not be necessary. Note that P is the summation of IPR diagonal values. The value of a sine IPR on the diagonals is a sine-squared; values much less thanmore » cardinal direction (m, n) values. This implies that cardinal direction interpolation requires higher precision than diagonal interpolation. Consequently, we employed a smaller set. The spatially interpolated SVA used an 8-point/4-point sine interpolator described above. Table 1 shows the Table 1 results show a two-times speed-up using the 1.3x oversampled and spatially interpolated SVA over the Figure 1d. Detected results of 1.3x oversampled sine interpolated spatially variant« less
  7. The Rapid Terrain Visualization Advanced Concept Technology Demonstration (RTV-ACTD) is designed to demonstrate the technologies and infrastructure to meet the Army requirement for rapid generation of digital topographic data to support emerging crisis or contingencies. The primary sensor for this mission is an interferometric synthetic aperture radar (IFSAR) designed at Sandia National Laboratories. This paper will outline the design of the system and its performance, and show some recent flight test results. The RTV IFSAR will meet DTED level III and IV specifications by using a multiple-baseline design and high-accuracy differential and carrier-phase GPS navigation. It includes innovative near-real-time DEMmore » production on-board the aircraft. The system is being flown on a deHavilland DHC-7 Army aircraft.« less
  8. A system for detecting defects on a moving web having a sequential series of identical frames uses an imaging device to form a real-time camera image of a frame and a comparitor to comparing elements of the camera image with corresponding elements of an image of an exemplar frame. The comparitor provides an acceptable indication if the pair of elements are determined to be statistically identical; and a defective indication if the pair of elements are determined to be statistically not identical. If the pair of elements is neither acceptable nor defective, the comparitor recursively compares the element of saidmore » exemplar frame with corresponding elements of other frames on said web until one of the acceptable or defective indications occur.« less
  9. A phase gradient autofocus system for use in synthetic aperture imaging accurately compensates for arbitrary phase errors in each imaged frame by locating highlighted areas and determining the phase disturbance or image spread associated with each of these highlight areas. An estimate of the image spread for each highlighted area in a line in the case of one dimensional processing or in a sector, in the case of two-dimensional processing, is determined. The phase error is determined using phase gradient processing. The phase error is then removed from the uncorrected image and the process is iteratively performed to substantially eliminatemore » phase errors which can degrade the image.« less

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