Collaboration with the University of Virginia (UVa) and the University of California, Santa Barbara (UCSB) has resulted in the collection of signature data in the THz region of the spectrum for ovalbumin, Bacillus Subtilis (BG) and RNA from MS2 phage. Two independent experimental measurement systems were used to characterize the bio-simulants. Prior to our efforts, only a limited signature database existed. The goal was to evaluate a larger ensemble of biological agent simulants (BG, MS2 and ovalbumin) by measuring their THz absorption spectra. UCSB used a photomixer spectrometer and UVa a Fourier Transform spectrometer to measure absorption spectra. Each group used different sample preparation techniques and made multiple measurements to provide reliable statistics. Data processing culminated in applying proprietary algorithms to develop detection filters for each simulant. Through a covariance matrix approach, the detection filters extract signatures over regions with strong absorption and ignore regions with large signature variation (noise). The discrimination capability of these filters was also tested. The probability of detection and false alarm for each simulant was analyzed by each simulant specific filter. We analyzed a limited set of Bacillus thuringiensis (BT) data (a near neighbor to BG) and were capable of discriminating between BT and BG. The signal processing and filter construction demonstrates signature specificity and filter discrimination capabilities.
In this paper we compare the performance for effluent gas detection of three types of imaging spectrometers. The spectrometers compared are the grating and Fourier Transform (FTS) for their wide implementation and the Acousto-Optic Tunable Filter (AOTF) for its unique feature of spectral tunability. The analysis is performed in the thermal emission region of spectra using Raytheon developed software simulation and modeling tools. The paper concludes with a proposed system design and architecture considerations for an AOTF-based hyperspectral and polarimetric imaging sensor for airborne and spaceborne platforms.
There is a need to assess hyperspectral image processing algorithms in a way that does not require applying the algorithm to a large set of spectral scenes. The statistical nature of hyperspectral scenes can be modeled as a set of means and covariances. In this model, each mean-covariance pair describes some physical component of the scene. Modeling the scene in this fashion allows non-gaussian nature of scene to be explored, with the assumption that the scene statistics are linear sums of gaussians. Once this component model of a scene is constructed, filter performance can be assessed quickly by applying the filter to the ensemble of means of covariances. Furthermore, filter performance can be predicted for scenes not yet collected, as scene models may be artificially generated from statistics of physical components. As a validation of the model we generate plots of target probability of detection versus probability of false alarm for natural scenes and models based on those scenes.
It was found, on orbit, that the Hubble Space Telescope had a conic constant error in the primary mirror. The result of the error is a substantial amount of spherical aberration in the image, significantly reducing image resolution and encircled energy. Parametric phase retrieval was the method used to determine the source of error and to find the magnitude from on-board camera images. The parameters which are estimated are a set of annular Zernike polynomials which are analogous to the classical aberrations. This was one of the first practical uses of phase retrieval for on-orbit measurement. This paper contains an overview of the algorithms, how they were used and the major results.
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