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Star trackers are the most accurate attitude sensors to perform 3-axis attitude estimation of spacecraft. However, even though they are specifically designed to estimate attitude, they can also be used to estimate the local coordinates of the camera on the Earth as well as the direction of East. This requires us to align the camera optical axis with the local direction of the gravity and to have knowledge of the time, that can be provided by an accurate clock. The resulting system is the Compass Star Tracker (CST), that is, a star tracker used as a global surface navigation system. This system would certainly not substitute the Global Positioning System which, in turn, does not suffer of the limitation night-only operation and in clear weather conditions. On the contrary, the CST does not require any satellite information or other support from ground stations to be used in lieu of the traditional Global Positioning System. [ABSTRACT FROM AUTHOR]

Direction-dependent scaling, shaping, and rotation of Gaussian basis functions are introduced for maximal trend sensing with minimal parameter representations for input output approximation. It is shown that shaping and rotation of the radial basis functions helps in reducing the total number of function units required to approximate any given input-output data, while improving accuracy. Several alternate formulations that enforce minimal parameterization of the most general radial basis functions are presented. A novel "directed graph" based algorithm is introduced to facilitate intelligent direction based learning and adaptation of the parameters appearing in the radial basis function network. Further, a parameter estimation algorithm is incorporated to establish starting estimates for the model parameters using multiple windows of the input-output data. The efficacy of direction-dependent shaping and rotation in function approximation is evaluated by modifying the minimal resource allocating network and considering different test examples. The examples are drawn from recent literature to benchmark the new algorithm versus existing methods. [ABSTRACT FROM AUTHOR]

Star identification can be accomplished by several different available algorithms that identify the stars observed by a star tracker. However, efficiency and reliability remain key issues and the availability of new active pixel cameras requires new approaches. Two novel algorithms for recursive mode star identification are presented here. The first approach is derived by the spherical polygon search (SP-search) algorithm, here used to access all the cataloged stars observed by the sensor field-of-view (FOV) and recursively add/remove candidate cataloged stars according the predicted image motion induced by camera attitude dynamics. Star identification is then accomplished by a star pattern matching technique which identifies the observed stars in the reference catalog. The second method uses star neighborhood information and a catalog neighborhood pointer matrix to access the star catalog. In the recursive star identification process, and under the assumption of "slow" attitude dynamics, only the stars in the neighborhood of previously identified stars are considered for star identification in the succeeding frames. Numerical tests are performed to validate the absolute and relative efficiency of the proposed methods. [ABSTRACT FROM AUTHOR]