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

Abstract

Radio Frequency (RF) technology represents a high-precision relative navigation solution that has significant potential for application to earth-orbiting satellites. In precision applications, 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 improvements to the accuracy of estimation based on multipath mitigation founded on the Gaussian mixture 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 implemented 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 multipath mitigation in RF relative measurement.