Orbit Determination for Partially Understood Object via Matched Filter Bank
Author(s)
Murphy, Timothy S.
Holzinger, Marcus J.
Flewelling, Brien R.
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Abstract
With knowledge of a space object's orbit, the matched filter is an image processing
technique which allows low signal-to-noise ratio objects to be detected. Many
space situational awareness research efforts have looked at ways to characterize
the probability density function of a partially understood space object. When prior
knowledge is only constrained to a probability density function, many matched
filter templates could be representative of the space object, necessitating a bank of
matched filters. This paper develops the measurement dissimilarity metric which
is then applied to partition a general prior set of orbits. A method for hypothesis
testing the result of a matched filter for a space object is developed. Finally, a
framework for orbit determination based on the matched filter result is developed.
Simulation shows that the analytic results enable more efficient computation and
a better framework for implementing matched filters.
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Date
2015-08
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Text
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Paper
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