In order to protect critical military and commercial space assets, the United States Space Surveillance Network must have the ability to positively identify and characterize all space objects. Unfortunately, positive identification and characterization of space objects is a manual and labor intensive process today since even large telescopes cannot provide resolved images of most space objects. The objective of this study was to collect and analyze visible-spectrum polarization data from unresolved images of geosynchronous satellites taken over various solar phase angles. Different collection geometries were used to evaluate the polarization contribution of solar arrays, thermal control materials, antennas, and the satellite bus as the solar phase angle changed. Since materials on space objects age due to the space environment, their polarization signature may change enough to allow discrimination of identical satellites launched at different times. Preliminary data suggests this optical signature may lead to positive identification or classification of each satellite by an automated process on a shorter timeline. The instrumentation used in this experiment was a United States Air Force Academy (USAFA) Department of Physics system that consists of a 20-inch Ritchey-Chrétien telescope and a dual focal plane optical train fed with a polarizing beam splitter. Following a rigorous calibration, polarization data was collected during two nights on eight geosynchronous satellites built by various manufacturers and launched several years apart. When Stokes parameters were plotted against time and solar phase angle, the data indicates that a polarization signature from unresolved images may have promise in classifying specific satellites.
A maximum a priori (MAP) estimation technique for the detection of focus aberrations in electro-optical imaging systems is developed. The technique simplifies the equipment required in focus aberration detection over previous methodologies. The magnitude of the focus aberration is estimated from a single image. The MAP estimation technique uses a Poisson distribution of the photons arriving at the detector from the object. A Gaussian distribution is added to the statistical model to account for the focus aberration. Using the imaging system statistical model and real laboratory images from a charge-coupled device (CCD) camera, the focus aberration detection (FAD) algorithm produces estimates of the focus aberrations. The results demonstrate a viable approach for estimation and potential removal of focus aberrations in electro-optical systems, without the need to divert any light from the primary channel, or for additional complicated equipment and associated calibration requirements.
Wave optics propagation codes are widely used to simulate the propagation of electromagnetic radiation through a turbulent medium. The basis of these codes is typically the two dimensional Fast Fourier Transform (FFT). Conventional FFTs (i.e. the standard Matlab FFT) do not use parallel processing and for large arrays, the processing time can be cumbersome. This research investigates the use of network- based parallel computing using personal computers. In particular, this study uses the Air Force Institute of Technology (AFIT) Bimodal Cluster a heterogeneous cluster of PCs connected by fast Ethernet for parallel digital signal processing using an FFT algorithm developed for use on this system. The parallel algorithms developed for the Parallel Distributed Computing Laboratory could greatly increase the computational power of current wave optics codes. The objective of this research is to implement current parallel FFT algorithms for use with wave optics propagation codes and quantify performance enhancement. With the parallel version of the FFT implemented into existing wave optics simulation code, high resolution simulations can be run in a fraction of the time currently required using conventional FFT algorithms. We present the results of implementing this parallel FFT algorithm and the enhanced performance achieved over the Matlab FFT2 function.