Precise formation-state estimation in low earth orbit using carrier differential GPS

Formation flying in Low Earth Orbit (LEO) offers great benefits for many space science applications. These formation flying missions will require precise relative navigation. The required precision can potentially be achieved using carrier differential GPS (CDGPS). However, previous to this work, no GPS-based sensor had demonstrated the capability of providing the necessary precision levels in LEO. This dissertation presents the design and demonstration of a decentralized, real-time CDGPS relative navigation system designed for the Orion formation flying microsatellite project. The navigation system achieves accuracy better than 2 cm for relative position in LEO formations with separations of 1 km between vehicles. This result is achieved by modifying the GPS receiver and designing an improved relative navigation filter. On the receiver, changes are made that allow it to operate reliably in LEO, and take synchronized, low-noise measurements. For the filter, implementation trade studies were performed to design an Extended Kalman Filter (EKF). These studies show that a nonlinear measurement update method is required, while a linear state propagation method can be used. For additional robustness, an adaptive algorithm is developed within the EKF, which is shown to correctly identify the covariance of the process and sensor noise. It also feeds the residuals back to the state covariance, which prevents filter divergence and maintains the covariance as a more accurate indicator of the performance of the nonlinear filter. Hardware-in-the-loop demonstrations are performed using Goddard Space Flight Center’s Formation Flying Test Bed (FFTB). The FFTB is the most realistic orbital test environment currently available. Four-vehicle formations were simulated, providing the first-ever simultaneous relative navigation results. These results show

[1]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[2]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[3]  N E Manos,et al.  Stochastic Models , 1960, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

[4]  R. Bucy,et al.  Filtering for stochastic processes with applications to guidance , 1968 .

[5]  Arthur Gelb,et al.  Applied Optimal Estimation , 1974 .

[6]  M. Kaplan Modern Spacecraft Dynamics and Control , 1976 .

[7]  Floyd M. Gardner,et al.  Phaselock techniques , 1984, IEEE Transactions on Systems, Man, and Cybernetics.

[8]  Graham C. Goodwin,et al.  Adaptive filtering prediction and control , 1984 .

[9]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[10]  James R. Wertz,et al.  Space Mission Analysis and Design , 1992 .

[11]  Robert F. Stengel,et al.  Optimal Control and Estimation , 1994 .

[12]  Elliott D. Kaplan Understanding GPS : principles and applications , 1996 .

[13]  Edgar Glenn Lightsey Development and flight demonstration of a GPS receiver for space , 1997 .

[14]  D. Vallado Fundamentals of Astrodynamics and Applications , 1997 .

[15]  Richard A. Brown,et al.  Introduction to random signals and applied kalman filtering (3rd ed , 2012 .

[16]  Zhang Hongbing GPS attitude determination algorithm and its software for spacecraft applications , 2000 .

[17]  Robert H. Bishop,et al.  Hardware-in-the-Loop GPS Test Facility for Spacecraft Autonomous Rendezvous , 2001 .

[18]  J. Russell Carpenter,et al.  Decentralized control of satellite formations , 2002 .