20 September 2016 Space object detection using Poisson distributed vector projections
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Abstract
The detection of near Earth space objects and potentially threatening asteroids is a challenging problem. The goal of this paper is to present a new method useful for detecting objects in space and to prove that it is more efficient than other existing methods. Instead of simply looking at a single, specified point in an image, this new method would use vectors created by summing across the rows and columns of the CCD array. The data is processed by a likelihood ratio test designed to process vector data under Poisson noise assumptions. The summing is done on the chip before readout noise is added to the image, so this method might have a higher signal to noise ratio than other techniques. Using this method, it is possible to search the same amount of space as you would with other techniques, while using less data. In order to prove that this method is more efficient, a likelihood ratio test will be derived and compared to the same kind of test using simulated data. It will be shown that this new method has a higher detection probability and lower false alarm rate than other methods under certain conditions; and that it will be more useful for detecting potentially deadly asteroids or other space objects that would threaten national security.
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Asher N. Cain, Asher N. Cain, } "Space object detection using Poisson distributed vector projections", Proc. SPIE 9982, Unconventional Imaging and Wavefront Sensing XII, 99820U (20 September 2016); doi: 10.1117/12.2236365; https://doi.org/10.1117/12.2236365
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