Abstract
The unscented Kalman filter (UKF) can effectively reduce the linearized model error and the dependence on initial coordinate values for indoor pseudolite (PL) positioning unlike the extended Kalman filter (EKF). However, PL observations are prone to various abnormalities because the indoor environment is usually complex. Standard UKF (SUKF) lacks resistance to frequent abnormal observations. This inadequacy brings difficulty in guaranteeing the accuracy and reliability of indoor PL positioning, especially for phase-based high-precision positioning. In this type of positioning, the ambiguity resolution (AR) will be difficult to achieve in the presence of abnormal observations. In this study, a robust UKF (RUKF) and partial AR (PAR) algorithm are introduced and applied in indoor PL positioning. First, the UKF is used for parameter estimation. Then, the anomaly recognition statistics and optimal ambiguity subset of PAR are constructed on the basis of the posterior residuals. The IGGIII scheme is adopted to weaken the influence of abnormal observation, and the PAR strategy is conducted in case of failure of the conventional PL-AR. The superiority of our proposed algorithm is validated using the measured indoor PL data for code-based differential PL (DPL) and phase-based real-time kinematic (RTK) positioning modes. Numerical results indicate that the positioning accuracy of RUKF-based indoor DPL is higher with a decimeter-level improvement compared that of the SUKF, especially in the presence of large gross errors. In terms of high-precision RTK positioning, RUKF can correctly identify centimeter-level anomalous observations and obtain a corresponding positioning accuracy improvement compared with the SUKF. When relatively large gross errors exist, the conventional method cannot easily realize PL-AR. By contrast, the combination of RUKF and the PAR algorithm can achieve PL-AR for the selected ambiguity subset successfully and can improve the positioning accuracy and reliability significantly. In summary, our proposed algorithm has certain resistance ability for abnormal observations. The indoor PL positioning of this algorithm outperforms that of the conventional method. Thus, the algorithm has some practical application value, especially for kinematic positioning.
Highlights
Pseudo-Satellite or Pseudolite, abbreviated as PL, is a transmitter deployed on the ground to transmit some kind of positioning signal, which usually transmits signals similar to navigation satellite system (GNSS) [1,2,3]
We propose a reliable indoor PL positioning method based on the robust UKF (RUKF) and partial AR (PAR) algorithm
The traditional parameter estimation method lacks the effective ability of anomaly recognition and anti-interference, and the conventional PL-ambiguity resolution (AR) becomes challenging
Summary
Pseudo-Satellite or Pseudolite, abbreviated as PL, is a transmitter deployed on the ground to transmit some kind of positioning signal, which usually transmits signals similar to navigation satellite system (GNSS) [1,2,3]. The essence is to control the gross errors by constructing equivalent weights for weakening the influence of abnormal errors on the solution This method reduces the contribution of anomalous observations to parameter estimation and can yield a relatively reliable result [35,36]. For indoor PL high-precision RTK, the RUKF will weaken the abnormal effects and improve the accuracy of float ambiguity solution when observation anomalies exist. We further combine the RUKF with PAR strategy to obtain reliable PL positioning results with certain robustness by the anomaly recognition statistics information and select an ambiguity subset for PL-AR in the presence of large gross errors in the carrier phase observation.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
