This paper evaluates the performance of a tightly coupled GPS/INS integrated system based on low cost MEMS IMUs in dense urban areas, and investigates two different methods to improve its performance. The first method used is to derive observations from two different constraint equations reflecting the behavior of a typical land vehicle. The first constraint equation is derived assuming that the vehicle does not slip and always remains in contact with the ground. If these assumptions are true the velocity of the vehicle in the plane perpendicular to the forward direction should be zero. The second constraint equation is derived from the fact that the height does not change much in a short time interval in a land vehicular environment. Thus, when a GPS outage occurs (partial/complete), the integrated system combines the INS and constraints-derived virtual measurements to keep the position and velocity errors bounded. This method is suitable for use in real-time applications. The second method is specifically suitable for a post-mission application and involves the use of Rauch-Tung-Striebel (RTS) smoother. The designed system performance is evaluated using two data sets collected in dense urban areas. The obtained results demonstrate the effectiveness of different algorithms considered, in controlling the INS error growth, and indicates the potential of MEMS IMUs for use in land vehicle navigation applications.
This article presents a new tracking technique for sine-BOC(n, n) (or Manchester encoded) ranging signals, which will most likely be part of the new European Global Navigation Satellite System (GNSS), Galileo, signal plan. When traditional sine-BOC(n, n) tracking is considered, although offering excellent performance compared with current signals, it has the main drawback of potentially giving biased measurements. The new method presented herein allows the removal of this threat while maintaining the same level of performance. An adapted version of this technique can also be used for acquisition purposes.
Multipath is a major source of error in high precision GlobalPositioning System (GPS) static and kinematic differential positioning. Multipath accounts for most of the total error budget in carrier phase measurements in a spacecraft attitude determination system. It is a major concern in reference stations, such as in Local Area Augmentation Systems (LAAS), whereby corrections generated by a reference station, which are based on multipath corrupted measurements, can significantly influence the position accuracy of differential users. Code range, carrier phase, and signal-to-noise (SNR) measurements are all affected by multipath, and the effect is spatially correlated within a small area. In order to estimate and remove code and carrier phase multipath, a system comprising a cluster of five GPS receivers and antennas is used at a reference station location. The spatial correlation of the receiver data, and the known geometry among the antennas, are exploited to estimate multipath for each satellite in each antenna in the system. Generic receiver code and carrier tracking loop discriminator functions are analyzed, and relationships between receiver data, such as code range, carrier phase, and SNR measurements, are formulated and related to various multipath parameters. A Kalman filter is described which uses a combination of the available information from the antennas (receivers) in the multiantenna cluster to estimate various multipath parameters. From the multipath parameters, the code range and carrier phase multipath is estimated and compensated. The technique is first tested on simulated data in a controlled multipath environment. Results are then presented using field data and show a significant reduction in multipath error.
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