Wrong Fix Detection for RTK Positioning Based on Relative Position Between Multiple Antennas

We propose a methodology that uses the relative positional information of multiple antennas to estimate the Wrong Fix, which refers to an erroneous determination of the carrier-phase ambiguity utilized in GNSS satellites. The proposed approach is based on the fundamental notion that the mutual positional relationship of multiple antennas mounted on a mobile robot remains constant, and it uses machine-learning techniques based on the relative position information among the antennas to identify instances of Wrong Fixes. The relative distance between the antennas is derived from the real-time kinematic (RTK) position information of each antenna. The confidence level of the RTK positioning results was calculated using logistic regression, considering the measurement error with respect to the true value. To determine the Wrong Fixes, a labeled dataset was constructed, indicating that data were categorized as wrong fixes when the error from the true value exceeded 0.1 m. This dataset served as the training database for the logistic regression model. Experimental results demonstrate that the proposed methodology effectively reduced the root mean squared error between the measured location, classified as fixed by a trained discriminator, and the true value.