The use of Global Positioning System (GPS) observables provides the primary and advantageous technique for satellite orbit determination in low earth orbit (LEO), in particular when onboard autonomy is required. A wide range of LEO spacecraft equipped with GPS receivers have been launched into space for different applications. Aiming at determining the position and velocity of the satellite in real-time, a consider Kalman filter (CKF)-based reduced-dynamic orbit determination (RDOD) is introduced in this paper. In the CKF, orbit dynamic model is simplified to meet the space-borne computational limitations. The atmospheric drag and solar radiation pressure coefficients are considered rather than estimated in the conventional RDOD strategy. Only a lower order and degree of an earth gravity model is used in the orbit model. However, the propagation of the covariance of the consider parameters is able to absorb the unmodeled and dismodeled perturbations. Therefore, the filter could become convergent with desirable orbit determination performance. The CKF-RDOD method is implemented with a set of the Gravity Recovery and Climate Experiment flight data. The solutions indicate that this proposed method could achieve satisfactory precision orbit determination with approximately 1.5 m level of three-dimensional root-mean-square error using GPS broadcast messages in real-time scenarios.

GPS-based onboard real-time orbit determination for leo satellites using consider Kalman filter

Observability analysis and autonomous navigation for two satellites with relative position measurements

Making use of dual-frequency (DF) global navigation satellite system (GNSS) observations and good dynamic models, the precise orbit determination (POD) for the satellites on low earth orbits has been intensively investigated in the last decades and has achieved an accuracy of centimeters. With the rapidly increasing number of the CubeSat missions in recent years, the POD of CubeSats were also attempted with combined dynamic models and GNSS DF observations. While comprehensive dynamic models are allowed to be used in the postprocessing mode, strong constraints on the data completeness, continuity, and restricted resources due to the power and size limits of CubeSats still hamper the high-accuracy POD. An analysis of these constraints and their impact on the achievable orbital accuracy thus needs to be considered in the planning phase. In this study, with the focus put on the use of DF GNSS data in postprocessing CubeSat POD, a detailed sensitivity analysis of the orbital accuracy was performed w.r.t. the data continuity, completeness, observation sampling interval, latency requirements, availability of the attitude information, and arc length. It is found that the overlapping of several constraints often causes a relatively large degradation in the orbital accuracy, especially when one of the constraints is related to a low duty-cycle of, e.g., below 40% of time. Assuming that the GNSS data is properly tracked except for the assumed constraints, and using the International GNSS Service (IGS) final products or products from the IGS real-time service, the 3D orbital accuracy for arcs of 6 h to 24 h should generally be within or around 1 dm, provided that the limitation on data is not too severe, i.e., with a duty-cycle not lower than 40% and an observation sampling interval not larger than 60 s.

A Sensitivity Study of POD Using Dual-Frequency GPS for CubeSats Data Limitation and Resources

Impact of GPS receiver antenna GRAPHIC residual variations on single-frequency orbit determination of LEO satellites

On-board orbit determination for low thrust LEO-MEO transfer by Consider Kalman Filtering and multi-constellation GNSS

The combination of GPS measurements and high-fidelity dynamic models via a Kalman filter/smoother, known as the reduced dynamic technique, allows 3D positioning of Low Earth Orbiters to the sub-decimeter level. Such accuracies can only be achieved if the GPS data are nearly continuous, post-processed and a dual-frequency receiver is utilized. The focus of this study is to quantitatively analyze the degradations in position accuracy in the presence of various limitations or constraints, which can be brought on by mission hardware limitations, for example, on micro- or nanosatellites. The constraints explored in this study are as follows: the use of single-frequency data only; real-time processing; limited dynamic modeling due to computing capabilities; and non-continuous GPS receiver operation due to power limits. The experiments are conducted with 6-h data arcs for 7 separate days using data from the CHAllenging Mini-Satellite Payload. A 3D root mean square (rms) error of 15 cm is observed in the best-case solution, in which dual-frequency data are post-processed with all available data. Various levels of accuracy degradations are observed as constraints are placed on this best-case solution. The 3D rms error of the post-processed, single-frequency solution is 68 cm and 1.3 m for the real-time, dual-frequency solution. In very challenging environments, for example, with the receiver on for only 10 min of a 90-min orbit, the 3D rms increases to 350 m.

Amandeep Mander

Papers

GPS-based precise orbit determination of Low Earth Orbiters with limited resources