Linear statistical methods for the processing of measurements on an increasing sample in the presence of errors in the spacecraft-motion model

Details Linear statistical methods for the processing of measurements on an increasing sample in the presence of errors in the spacecraft-motion model Linear statistical methods for the processing of measurements on an increasing sample in the presence of errors in the spacecraft-motion model

Jul 1985

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1985kosis..23..535n&link_type=abstract

Kosmicheskie Issledovaniia (ISSN 0023-4206), vol. 23, July-Aug. 1985, p. 535-546. In Russian.

Computer Science

Error Correcting Codes, Kalman Filters, Linear Prediction, Space Navigation, Spacecraft Trajectories, Trajectory Measurement, Algorithms, Boundary Value Problems, Data Sampling, Dynamic Programming, Recursive Functions, State Estimation, State Vectors, Wiener Hopf Equations

Scientific paper

An algorithm for processing full-sample measurements in the state-vector estimation of a spacecraft with errors in the motion model is presented. A rigorous solution of the navigation problem requires the examination of a set of nonlinear two-point boundary value problems, which in the linear case yield recursive Kalman-Bucy algorithms. A theorem is demonstrated concerning the equivalence of estimates obtained through the solution of linear two-point boundary value problems and estimates obtained through the processing of the full sample of measurements or an increasing sample with inclusion of the error vector in the motion model. Equations for an increasing sample with an expanded state vector due to errors in the motion model are compared with estimates obtained through the solution of the Wiener-Hopf matrix equation without an increase in the dimensionality of the state vector.

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