Publications
Volume 11, Number 2
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Synthetic Aperture Radar Image Coding Many factors govern the design of image-coding systems, ranging from sensor type through storage and transmission limitations to imagery end use. Image-compression performance depends on the costs of storage or transmission bandwidth (in bits per pixel), computational burden (in operations per second), information loss (utility), and artifacts that may change the receiver operating characteristics (probability of detection versus number of false alarms per area). We describe how general image-coding concepts are specialized for synthetic aperture radar (SAR) imagery, and we discuss design trade-offs with respect to representation systems, quantizers, and bit allocation. We also discuss criteria and techniques for evaluating the performance of compression systems. Photonic-Crystal Antenna Substrates A recently developed artificial dielectric, called a photonic crystal, provides an elegant and effective solution to the problem of fabricating high-performance planar antennas on substrates having high dielectric constants. The stop band of the photonic crystal rejects the majority of power radiated by an antenna mounted on its surface, without shorting out the driving-point impedance. This behavior makes the antenna more efficient than if it is mounted on a homogeneous substrate consisting of the same dielectric material as the photonic crystal. We measured antenna characteristics for bow-tie and dipole antennas on two types of photonic crystal: (1) conventional photonic crystals consisting of a periodic array of holes filled with air and embedded in a high dielectric material, and (2) metallodielectric photonic crystals consisting of a periodic array of metallic spheres embedded in a low dielectric material. In some cases, the directive gain can greatly exceed that of the same antenna suspended in free space. An Architecture for Semi-Automated Radar Image Exploitation To improve the timeliness and accuracy of synthetic aperture radar image exploitation, DARPA started the Monitor program at Lincoln Laboratory. This program was combined with related technologies from other contractors to develop the Semi-Automated IMINT (image intelligence) Processing (SAIP) system. The SAIP system accepts radar images in real time, distributes them to a number of algorithms for automated analysis, and organizes the images and algorithmic results for display to image analysts. The SAIP software infrastructure, which mediates between the operating system and the application code of the individual components, supports message passing between those components and allows the system to operate as a pipeline of parallel computation modules. In this article we describe the design considerations of the overall system architecture. We also discuss how the component systems are organized and how the software allows many components from different organizations to work together. The Space-Based Visible Program A principal sensor on board the Midcourse Space Experiment satellite, launched in 1996, is the Space-Based Visible (SBV) sensor, a visible-band electro-optical camera designed at Lincoln Laboratory to perform the first demonstration of space-based space surveillance. The task of the SBV sensor is to gather metric and photometric information on a variety of resident space objects (RSO). In 1997, the SBV sensor was transitioned to a Contributing Sensor in the Space Surveillance Network (SSN). Since April 1998, the SBV sensor has responded to daily tasking requests in support of routine RSO catalog maintenance, making it the first operational space-based space-surveillance sensor. With its orbital location, wide field of view, and high metric accuracy, the SBV sensor has made a significant contribution to the SSN, providing more tracks of objects in the geosynchronous belt than any other sensor. |
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