SCIDAR (SCIntillation Detection And Ranging) is a technique for recovering atmospheric C<SUB>n</SUB><SUP>2</SUP> profiles from the scintillation pattern in or near the telescope's entrance pupil plane. The generalized SCIDAR technique allows the measurement of the entire atmospheric C<SUB>n</SUB><SUP>2</SUP> profile. We report on a program of generalized SCIDAR measurements at the Air Force Maui Optical Station (AMOS). We discuss how a short exposure imaging system at AMOS was modified to record SCIDAR data. We also discuss the use of computer simulations of atmospheric propagation to aid in the development of data analysis techniques. Results from the first set of SCIDAR observations are presented.
We present preliminary results from a comparison of image estimation and recovery algorithms developed for use with advanced telescope instrumentation and adaptive optics systems. Our study will quantitatively compare the potential of these techniques to boost the resolution of imagery obtained with undersampled or low-bandwidth adaptive optics; example applications are optical observations with IR- optimized AO, AO observations in server turbulence, and AO observations with dim guidestars. We will compare the algorithms in terms of morphological and relative radiometric accuracy as well as computational efficiency. Here, we present qualitative comments on image results for two levels each of seeing, object brightness, and AO compensation/wavefront sensing.
Images of astronomical objects acquired by ground-based telescopes are blurred by atmospheric turbulence. These blurring effects can be partially overcome by post-detection processing such as speckle imaging (SI). We have developed a parallel implementation of SI to dramatically reduce the time required to reduce imaging data, allowing us to implement a near realtime (NRT) SI image feedback capability. With NRT SI feedback, telescope operators can select observing parameters to optimize data quality while the data is being taken. NRT processing also allows easy selection of the best data from a long observation for later post-detection processing using more sophisticated algorithms. Similar NRT schemes could also be implemented for non-imaging measurements, such as spectroscopy. NRT feedback will yield higher quality data products and better utilization of observatory resources.
Space-object identification from ground-based telescopes is challenging because of the degradation in resolution arising from atmospheric turbulence. Phase-diverse speckle is a novel post-detection correction method that can be used to overcome turbulence-induced aberrations for telescopes with or without adaptive optics. We present a simulation study of phase-diverse speckle satellite reconstructions for the Air Force Maui Optical station 1.6-meter telescope. For a given turbulence strength, satellite reconstruction fidelity is evaluated as a function of quality and quantity of data. The credibility of this study is enhanced by reconstructions from actual compensated data collected wit the 1.5-meter telescope at the Starfire Optical Range. Consistent details observed across a time series of reconstructions from a portion of a satellite pass enhance the authenticity of these features. We conclude that phase-diverse speckle can restore fine-resolution features not apparent in the raw aberrated images of space objects.
We describe a model-based image analysis system which automatically estimates the 3D orientation vector of satellites and their sub-components by analyzing images obtained from a ground-based optical surveillance system. We adopt a two-step approach: pose estimates are derived from comparisons with a model database; pose refinements are derived from photogrammetric information. The model database is formed by representing each available training image by a set of derived geometric primitives. To obtain fast access to the model database and to increase the probability of early successful matching, a novel index hashing method is introduced. We present recent results which include our efforts at isolating and estimating orientation vectors from degraded imagery on a significant database of satellites. We also discuss the problems our system encounters with some of the images, and the solutions we are implementing to significantly improve the system.
Extracting satellite solar-array and main-body orientation vector information from optical imagery is an integral part of space object identification analysis. We describe a model-based image analysis system which automatically estimates the 3-D orientation vector of satellites by analyzing images obtained from ground-based optical telescopes. We adopt a two-step approach. First, pose estimates are derived from comparisons with a model database, and second, pose refinements are derived from photogrammetric information. The model database is formed by representing each available training image by a set of derived geometric primitives. To obtain fast access to the model database and to increase the probability of early successful matching, a novel indexing method is introduced. We present our preliminary results, evaluate the overall performance of the technique, and suggest improvements.
This paper demonstrates the application of new pattern recognition techniques that can be used to characterize space objects. The feature space trajectory neural network (FST NN) was first presented by Leonard Neiberg and David P. Casasent in 1994 as a target identification tool. Kenneth H. Fielding and Dennis W. Ruck recently applied the hidden Markov model (HMM) classifier to a 3D moving light display identification problem and a target recognition problem, using time history information to improve classification results. This paper shows how the FST NN and HMM can be used to automate optical detection of space object anomalies. Time sequenced images produced by a simulation program are used for testing these anomaly detection algorithms. Two data sets are tested. The second data set is tested with various levels of shot noise. The first data set is more difficult to classify, with the best FST NN test achieving a 100% anomaly detection rate with 5% false alarm rate. FST NN and HMM tests on the second data set achieved 100% anomaly detection with no false alarms. With various levels of shot noise added, the FST NN achieves a 100% anomaly detection rate with 4% false alarm rate. A variety of Fourier features are tested with energy normalized low frequency coefficients producing the best consistent results across data sets and noise levels. A new FST NN test is presented that measures how well the order of a test sequence matches other sequences in the database. The original FST NN is based strictly on feature space distance, but when the order of the sequence is important, the new test is useful.
Several observations of atmospheric turbulence statistics have been reported which do not obey Kolmogorov's power spectral density model. These observations have prompted the study of optical propagation through turbulence described by non-classical power spectra. This paper presents an analysis of optical propagation through turbulence which causes fluctuations in the index of refraction. The index fluctuations are assumed to have spatial power spectra that obey arbitrary power laws. The spherical and plane wave structure functions are derived using Mellin transform techniques. The wave structure function is used to compute the Strehl ratio of a focused plane wave propagating in turbulence as the power law for the spectrum of the index of refraction fluctuations is varied from -3 to -4. The relative contributions of the log amplitude and phase structure functions to the wave structure function are computed. At power laws close to -3, the magnitude of the log amplitude and phase perturbations are determined by the system Fresnel ratio. At power laws approaching -4, phase effects dominate in the form of random tilts.