Shadow Imaging Efforts at MIT Lincoln Laboratory
Abstract
The technique of shadow imaging allows one to take pictures of distant objects that are beyond the reach of the largest ground-based astronomical telescopes. The technique relies on the fact that stars are nearly ideal point sources and are conveniently located behind man-made deep space objects, such as satellites. As a space object passes in front of a star (an "occultation event"), the object casts a shadow on the ground - a diffraction pattern that can be inverted to reveal the silhouette of the object. This idea of reconstructing the image from the shadow on the ground was first proposed by Burns et al. (2005, Proc. SPIE 5896), but the idea has never been implemented nor has its feasibility been studied in detail. There is no doubt that the technique is very challenging: the ability to predict the location of and capture meter-size shadows (roughly the size of the space object) that travel at 0.5 km/s across the surface of the Earth has never been demonstrated experimentally; furthermore, effects such as turbulence, background light, non-monochromatic light, and atmospheric dispersion have not been addressed or even considered. We have addressed these issues and our simulations show that, among other capabilities, the technique offers near diffraction-limited "imaging" from the ground, with the resolution limited only by the angular size of the occulted star. The technique is also remarkably robust against atmospheric turbulence. We are thus proposing shadow imaging as an alternative method to image deep space objects that elude ground-based optical telescopes and radars, such as non-spinning geosynchronous satellites. Most man-made satellites can be imaged with centimeter resolution by existing radars, but geosatellites that display very little motion cannot be imaged by radar and there is still no good solution for this problem.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 2008
- Accession Number
- ADA532041
Entities
People
- Bert Willard
- Jane Luu
- Leaf Jiang
Organizations
- Massachusetts Institute of Technology