Small field-of-view star identification using Bayesian decision theory

We describe a simple autonomous star identification algorithm which is effective using a narrow field of view (FOV) (2 deg), making the use of a science camera for star identification feasible. This work extends that of Padgett and Kreutz-Delgado (1997) by setting decision thresholds using Bayesian decision theory. Our simulations show that when positional accuracy of imaged stars is 0.5 pixel (standard deviation) and the apparent brightness deviates by 0.8 unit stellar magnitude, the algorithm correctly identifies 96.0% of the sensor orientations, with less than a 0.3% rate of false positives.