Enhancing ground based telescope performance with image processing.

摘要

The Space Surveillance Telescope (SST) is a Defense Advanced Research Projects Agency (DARPA) program designed to detect objects in space like Near Earth Asteroids (NEAs) and space debris in the Geosynchronous Earth Orbit (GEO) belt. Binary hypothesis tests (BHTs) have historically been used to facilitate the detection of new objects in space. In this dissertation, a multi-hypothesis test (MHT) detection strategy is introduced to improve the detection performance of the SST. In this context, the MHT determines if an unresolvable point source is in the center, corner or side of a pixel in contrast to a BHT, which only tests whether an object is in the pixel or not. An experiment, recording observations of a known GEO satellite as it enters eclipse, is used to demonstrate improved probability of detection with the MHT by as much as 50% over existing BHT methods.;In order to achieve optimal performance of the SST, alignment of the telescope is conducted by retrieving phase information from defocused point sources to determine the telescope's aberrations and then the mirrors are moved for optical correction. A new direct search phase retrieval technique for determining the optical prescription of an imaging system in terms of Zernike coefficients is described. The technique provides coefficient estimates without the need to defocus point source images to generate phase diversity by using electric field estimates in addition to intensity data. Simulated point source data shows the new phase retrieval algorithm avoids getting trapped in local minima over a wide range of random aberrations. Experimental point source data are used to demonstrate the phase retrieval effectiveness.