The United States Air Force Academy (USAFA) has developed techniques to characterize and identify satellites since 2014 using a 16-inch telescope on campus, as well as off-campus telescopes which comprise the Falcon Telescope Network. USAFA is upgrading its Falcon telescopes with new dual filter wheels and cameras. This will require updated calibrations for the new equipment along with a new capability to combine hyperspectral and polarimetric observations. For polarization calibration, highly accurate star fields are used. Emission and absorption stars will be used to calibrate the hyperspectral observations. Once the calibrations are updated, the combined observations with the new dual filter wheels will be used to make observations of geosynchronous satellites. This effort focuses on these combined hyperspectral-polarimetric observations, characterizing differences observed between satellites, and development of techniques to exploit these differences for the identification or discrimination of satellites.
The United States Air Force Academy (USAFA) operates the Falcon Telescope Network (FTN) to support its research program in the utility of satellite optical signatures in Space Situational Awareness. In addition to collecting photometric, spectroscopic, and polarimetric data, the FTN sensors which are equipped with diffraction grating elements also operate as slitless spectrographs. FTN spectroscopic data has been used to demonstrate that it can effectively distinguish different stable geosynchronous satellites (GEO). Because the attitude of the GEO's unarticulated parts (e.g. bus) and the axis of rotation of the articulated parts (e.g., solar panel) are predominantly fixed, the light curves and the time-resolved spectra are expected to be nearly repeatable from night to night. Furthermore, the spectra of GEOs may be effective identifying signatures. To demonstrate the ability to distinguish GEOs using spectroscopic data, we reduce the spectra to vectors of features with smaller dimensionality. That can be accomplished by applying a linear dimensionality-reduction technique, e.g., Principal Component Analysis (PCA) or using a physics-based transformation that consists of smoothing and under-sampling the spectra. The PCA features consists of up to the five most prominent principal components. The physics-based feature vector is the smoothed GEO spectral reflectance sampled at 37 fixed and equally spaced wavelengths. The first approach also generates a visualizable 2-dimensional representation using the first two PCA components, while the second approach preserves as much information as allowed by the effective spectrograph's resolution. Using satellite names or numbers as labels of the classes, we trained a number of classifiers with the GEO's feature vectors. Our analyses showed that multi-GEO classification can achieve accuracy as high as 98%. We also demonstrated that instead of collecting many spectra in the range of solar phase angles as training data, we can synthesize training spectra with a limited number of reference spectra and still achieve satisfactory classification accuracies.
This paper presents preliminary results on the characterization of DirecTV-10 satellite with photometric observations during a glint season from 04:00 – 08:00 UTC on 23 February 2021 with U.S. Air Force Academy’s USAFA-16 telescope and simulations of the scene with the physics-based simulator; Digital Imaging and Remote Sensing Image Generation (DIRSIGTM) developed by the Rochester Institute of Technology (RIT). The objective of this work is to find the best set of operator-tuned parameters needed by the simulator to match as close as possible to observations. To accomplish this, the parameters of the optical system, the latitude/longitude and altitude of the telescope, the two-line element (TLE) set of the satellite, and atmospheric conditions at the time of the observation are input into DIRSIGTM to carry out the simulations. Furthermore, it is assumed that all parameters remain constant throughout the observations. The optical system USAFA-16 is a small aperture telescope equipped with a filter wheel which provides photometric, spectroscopic, and polarimetric images of the satellite. The results reported in this paper consist of an effort to correlate wide-band photometric images of the satellite with simulated images of these same wavebands. We use a high-fidelity CAD model of the satellite, and material properties such as pristine reflectance values, and BRDF measurements of the many components of the model which are provided by the Air Force Research Laboratory (AFRL), and ancillary information. We show preliminary results that demonstrate that DIRSIGTM may be used to characterize the satellite to some degree through the process of correlating calibrated magnitude patterns observed on photometric images. Further investigation is required to do the search of parameters in a systematic way, and move towards better agreement between observed and simulated data.
Tracking and classifying objects in the space domain is important for the military due to increased concern of congestion from commercial and national interests. This research use a 100-lines-per-millimeter diffraction grating to perform slitless spectroscopy across a network of telescopes to collect satellite spectral signatures. The pixel-to-wavelength relationship for the diffraction gratings are characterized using calibration stars with known spectral features. Once calibrated, the United States Air Force Academy (USAFA) telescopes are used to observe manmade satellites with the goal to characterize them based on their reflected spectra.
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.
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