Multipath mitigation method based on Gaussian mixture model in RF relative measurement

摘要

Radio Frequency (RF) technology represents a high-precision relative navigation solu-tion that has significant potential for application to earth-orbiting satellites. In precision applica-tions, multipath errors dominate the total error because observables, which are used to estimate carrier-phase integer ambiguity, are not always subject to a Gaussian distribution when dual-frequency ambiguity estimation methods are used in the presence of multipath. As it has been shown that ranging observables obey a Gaussian mixture distribution, this study proposes improve-ments to the accuracy of estimation based on multipath mitigation founded on the Gaussian mix-ture model. To this end, such a model is created for integer ambiguity resolution in the presence of multipath, using which the theoretical error in dual-frequency ambiguity estimation is derived. Expectation Maximization (EM), which aids dual-frequency ambiguity estimation, is subsequently proposed to reduce the effect of multipath errors. Finally, two experimental scenarios are imple-mented to test the performance of the proposed method. The results show that EM-aided dual-frequency ambiguity estimation reduces the range error to approximately 20％in comparison with simple dual-frequency ambiguity estimation. Therefore the proposed technique is effective for mul-tipath mitigation in RF relative measurement.