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Application of Kalman filtering to spacecraft range residual prediction

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2 Author(s)
Madrid, G. ; California Institute of Technology, Pasadena, CA, USA ; Bierman, G.J.

One function of the Deep Space Network is validation of the range data that they receive. In this short paper we present an automated online sequential range predictor which shows promise of significantly reducing computational and manpower expenditures. The proposed algorithm, aU-Dcovariance factored Kalman filter, is demonstrated by processing a four month record of Viking spacecraft data taken enroute to Mars.

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Automatic Control, IEEE Transactions on  (Volume:23 ,  Issue: 3 )