GPS relative navigation filters could benefit notably from an accurate modeling of the ionospheric delays, especially over large baselines (>100 km) where double difference delays can be higher than several carrier wavelengths. This paper analyzes the capability of ionospheric path delay models proposed for spaceborne GPS receivers in predicting both zero-difference and double difference ionospheric delays. We specifically refer to relatively simple ionospheric models, which are suitable for real-time filtering schemes. Specifically, two ionospheric delay models are evaluated, one assuming an isotropic electron density and the other considering the effect on the electron density of the Sun aspect angle. The prediction capability of these models is investigated by comparing predicted ionospheric delays with measured ones on real flight data from the Gravity Recovery and Climate Experiment mission, in which two satellites fly separated of more than 200 km. Results demonstrate that both models exhibit a correlation in the excess of 80% between predicted and measured double-difference ionospheric delays. Despite its higher simplicity, the isotropic model performs better than the model including the Sun effect, being able to predict double differenced delays with accuracy smaller than the carrier wavelength in most cases. The model is thus fit for supporting integer ambiguity fixing in real-time filters for relative navigation over large baselines. Concerning zero-difference ionospheric delays, results demonstrate that delays predicted by the isotropic model are highly correlated (around 90%) with those estimated using GPS measurements. However, the difference between predicted and measured delays has a root mean square error in the excess of 30 cm. Thus, the zero-difference ionospheric delays model is not likely to be an alternative to methods exploiting carrier-phase observables for cancelling out the ionosphere contribution in single-frequency absolute navigation filters

This paper deals with the GPS-based relative navigation of LEO formations. Specifically, we consider applications characterized by two co-flying satellites with a large and highly variable separation, which are relevant to next generation monostatic/bistatic Synthetic Aperture Radar missions. In these applications, both scientific goals and control needs require the determination of the relative state with high accuracy. To this end, an Extended Kalman Filter is developed that processes double-difference pseudorange and carrier phase measurements on L1 and L2 frequencies. To preserve accuracy and robustness of the integer solution problem against large variations of the baseline, an original approach is developed in which, for each receiver, the Vertical Total Electron Content is included in the filter state. In addition, the double-difference ambiguities are re-estimated by the filter at each time step. A major technical problem of a filter processing double-differences is re-organizing the filter state when the pivot satellite changes. This is solved by an original and effective procedure that speeds up the filter convergence. Once the floating point estimates of the double-difference ambiguities have been produced by the dynamic filter, their integer values are extracted with the Least-Square Ambiguity Decorrelation Adjustment method and processed within a kinematic filter to estimate the relative position with high accuracy. Filter robustness and performance are evaluated by means of Monte Carlo simulations performed on the reference orbital scenario identified within the Italian SABRINA mission study. Results show that the integer ambiguities are always resolved, allowing to achieve a centimeter-level accuracy in all the simulated conditions.

This paper deals with the problem of real-time onboard relative positioning of low-Earth-orbit spacecraft over long\ud baselines using the Global Positioning System. Large intersatellite separations, up to hundreds of kilometers, are of\ud interest to multistatic and bistatic synthetic-aperture radar applications, in which highly accurate relative positioning\ud may be required in spite of the long baseline. To compute the baseline with high accuracy, the integer nature of dualfrequency,\ud double-difference carrier-phase ambiguities can be exploited. However, the large intersatellite separation\ud complicates the integer-ambiguities determination task due to the presence of significant differential ionospheric\ud delays and broadcast ephemeris errors. To overcome this problem, an original approach is proposed, combining an\ud extended Kalman filter with an integer least-square estimator in a closed-loop scheme, capable of fast on-the-fly\ud integer-ambiguities resolution. These integer solutions are then used to compute the relative positions with a singleepoch\ud kinematic least-square algorithm that processes ionospheric-free combinations of debiased carrier-phase\ud measurements. Approach performance and robustness are assessed by using the flight data of the Gravity Recovery\ud and Climate Experiment mission. Results show that the baseline can be computed in real time with decimeter-level\ud accuracy in different operating conditions

This paper deals with star tracker algorithms validation based on star field scene simulation and hardware-in-the-loop test configuration. A laboratory facility for indoor tests, based on the simulation of star field scenes, is presented. Attainable performance is analyzed theoretically for both static and dynamic simulations. Also, a test campaign is presented, in which a star sensor prototype with real-time, fully autonomous capability is exploited. Results that assess star field scene simulation performance and show the achievable validation for the sensor algorithms and performance in different operating modes (autonomous attitude acquisition, attitude tracking, and angular rate-only) and different aspects (coverage, reliability, and measurement performance) are discussed.

This paper describes a carrier-phase differential GPS approach for real-time relative navigation of LEO satellites flying in formation with large separations. These applications are characterized indeed by a highly varying number of GPS satellites in common view and large ionospheric differential errors, which significantly impact relative navigation performance and robustness. To achieve high relative positioning accuracy a navigation algorithm is proposed which processes double-difference code and carrier measurements on two frequencies, to fully exploit the integer nature of the related ambiguities. Specifically, a closed-loop scheme is proposed in which fixed estimates of the baseline and integer ambiguities produced by means of a partial integer fixing step are fed back to an Extended Kalman Filter for improving the float estimate at successive time instants. The approach also benefits from the inclusion in the filter state of the differential ionospheric delay in terms of the Vertical Total Electron Content of each satellite. The navigation algorithm performance is tested on actual flight data from GRACE mission. Results demonstrate the effectiveness of the proposed approach in managing integer unknowns in conjunction with Extended Kalman Filtering, and that centimeter-level accuracy can be achieved in real-time also with large separations. (c) 2013 IAA. Published by Elsevier Ltd. All rights reserved