Searching for a target among a large set of candidate orbits is a difficult and time consuming problem. Considering orbital dynamics, sensor uncertainties and the initial size of candidate location distribution, it is desirable to develop efficient search techniques. In this work, information theoretic methods for searching for a target in a large probability distribution using a space based sensor is considered. One intuitive approach is to steer the sensor towards regions of high probability density. Alternatively, information-theoretic methods steer the sensor based on metrics of the information gain in the posterior probability distribution. Through simulation, it is shown that information-theoretic search methods produce greater knowledge about probability distribution of the target's orbit.
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