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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