Li Xin, Huang Guanwen, Zhang Peng, Zhang Qin
College of Geology Engineering and Geomantic, Chang'an University, Xi'an 710054, China.
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China.
Sensors (Basel). 2019 Aug 25;19(17):3692. doi: 10.3390/s19173692.
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.
与扩展卡尔曼滤波器（EKF）不同，无迹卡尔曼滤波器（UKF）能有效减少室内伪卫星（PL）定位中的线性化模型误差以及对初始坐标值的依赖。然而，由于室内环境通常复杂，PL观测容易出现各种异常情况。标准UKF（SUKF）缺乏对频繁异常观测的抗性。这种不足给保证室内PL定位的准确性和可靠性带来困难，尤其是对于基于相位的高精度定位。在这种类型的定位中，存在异常观测时模糊度解算（AR）将难以实现。在本研究中，引入了一种鲁棒UKF（RUKF）和部分AR（PAR）算法并应用于室内PL定位。首先，使用UKF进行参数估计。然后，基于后验残差构建PAR的异常识别统计量和最优模糊度子集。采用IGGIII方案减弱异常观测的影响，并在传统PL-AR失败的情况下实施PAR策略。使用实测的室内PL数据针对基于码的差分PL（DPL）和基于相位的实时动态（RTK）定位模式验证了我们提出算法的优越性。数值结果表明，基于RUKF的室内DPL定位精度更高，与SUKF相比有分米级的提升，特别是在存在大的粗差时。就高精度RTK定位而言，RUKF能正确识别厘米级的异常观测，并与SUKF相比获得相应的定位精度提升。当存在相对较大的粗差时，传统方法难以实现PL-AR。相比之下，RUKF和PAR算法的组合能成功实现所选模糊度子集的PL-AR，并能显著提高定位精度和可靠性。总之，我们提出的算法对异常观测具有一定的抗性能力。该算法的室内PL定位性能优于传统方法。因此，该算法具有一定的实际应用价值，尤其是对于动态定位。
