The availability of GPS signals is a major concern for many existing and potential applications.
The integration of globe navigation satellite system ͑GNSS͒ with inertial navigation system ͑INS͒ is being heavily investigated as it can deliver more robust and reliable systems than either of the individual systems. In order to ensure the integrity of navigation solutions, it is necessary to incorporate an effective quality control scheme which uses redundant information provided by both the measurement and dynamic models. As the GNSS receiver autonomous integrity monitoring ͑RAIM͒ algorithms are well developed, here they are adapted to integrated GNSS/INS systems referred as extended RAIM ͑eRAIM͒, which are derived from the least-squares estimators of the state parameters in a Kalman filter, to assess GNSS/INS performance for a tightly coupled scenario. In addition to the RAIM capabilities, eRAIM procedures are able to detect faults in the dynamic model and isolate them from the measurement model. The analysis includes outlier detection and identification capabilities, reliability and separability measures of integrated GNSS/INS systems. The performance of the system is also investigated with respect to diminishing satellite visibility conditions.
With the European Commission (EC) and European Space Agency's (ESA) plans to develop a new satellite navigation system, Galileo and the modernisation of GPS well underway the integrity of such systems is as much, if not more, of a concern as ever. Receiver Autonomous Integrity Monitoring (RAIM) refers to the integrity monitoring of the GPS/Galileo navigation signals autonomously performed by the receiver independent of any external reference systems, apart from the navigation signals themselves. Quality measures need to be used to evaluate the RAIM performance at different locations and under various navigation modes, such as GPS only and GPS/Galileo integration, etc. The quality measures should include both the reliability and localizability measures. Reliability is used to assess the capability of GPS/ Galileo receivers to detect the outliers while localizability is used to determine the capability of GPS/Galileo receivers to correctly identify the detected outlier from the measurements processed.Within this paper, the fundamental equations required for effective outlier detection and identification algorithms are described together with the measures of reliability and localizability. Detailed simulations and analyses have been performed to assess the performances of GPS only and integrated GPS/Galileo navigation solutions with respect to reliability and localizability. Simulation results show that, in comparison with the GPS-only solution, the localizability of the integrated GPS/Galileo solution can be improved by up to 270%. The results also indicate an expectation of a considerable increase in the sensitivity to outliers and accuracy of their estimation with the augmentation of the Galileo system with the existing GPS system. K E Y
Traditionally, GNSS receiver autonomous integrity monitoring (RAIM) has been based upon single epoch solutions. RAIM can be improved considerably when available dynamic information is fused together with the GNSS range measurements in a Kalman filter. However, while the Kalman filtering technique is widely accepted to provide optimal estimates for the navigation parameters of a dynamic platform, assuming the state and observation models are correct, it is still susceptible to unmodelled errors. Furthermore, significant deviations from the assumed models for dynamic systems may also occur. It is therefore necessary that the state estimation procedure is complemented with effective and reliable integrity measures capable of identifying both measurement and modelling errors. Within this paper, fundamental equations required for the effective detection and identification of outliers in a kinematic GNSS positioning and navigation system are described together with the reliability and separability measures. These quality measures are implemented using a Kalman filtering procedure formulated with Gauss-Markov models where the state estimates are derived from least squares principles. Detailed simulations and analyses have been performed to assess the impact of the dynamic information on GNSS RAIM with respect to outlier detection and identification, reliability and separability. The ability of the RAIM algorithms to detect and identify dynamic modelling errors is also investigated. K E Y
Abstract. The integrated GPS/INS system has become an indispensable tool for providing precise and continuous position, velocity, and attitude information for many positioning and navigation applications. Therefore, it is important to gain insights into the characteristics of the integrated GPS/INS system performance, particularly their relationships with key operational factors, such as the trajectory and dynamics. Such knowledge can be used to improve the quality of positioning and navigation results from integrated GPS/INS systems. In order to analyse the influence of vehicle dynamics and trajectory, simulation and field tests have been carried out in this research. The test results show that the vehicle dynamic changes significantly affect the Kalman filter initialisation time and estimation performance depending on the system operational environments.
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