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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.

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]

Abstract: The problem of real-time, on-board star pattern identification is considered which must precede any spacecraft attitude estimation algorithm based on measured line of sight directions to stars. Following the use of the search-less -vector method to access feasible candidate stars (detailed in a previous paper) a tiered logical structure is introduced in which inter-star angles for pairs, triples and general elementary star configurations are used to match measured patterns to corresponding patterns in a star catalog. Analytical expressions are developed for the expected frequency of matching an observed star pattern with an incorrect pattern in the star catalog due to measurement error. Such knowledge of erroneous match frequencies can be used to rigorously terminate the star identification process with a virtually certain match, while obviating the need for expensive Monte Carlo simulations. Developments shown are supported with a few simulation results. [Copyright &y& Elsevier]