Zhu Huizhong, Fan Jiabao, Li Jun, Li Bo
School of Geomatics, Liaoning Technical University (LNTU), Fuxin 123000, China.
Sensors (Basel). 2024 Feb 24;24(5):1477. doi: 10.3390/s24051477.
The performance of low-cost smart terminals is limited by the performance of their low-cost Global Navigation Satellite System (GNSS) hardware and chips, as well as by the impact of complex urban environments, which affect the positioning accuracy and stability of GNSS services. To this end, this paper proposes a robust adaptive Kalman filter for different environments that can be applied after data preprocessing. Based on the Kalman filter algorithm, a robust estimation approach is introduced into real-time kinematic (RTK) positioning to make judgments on the abnormal observation values of low-cost smart terminals, which amplifies the variance and covariance of the outlier observation equation, and reduces the impact of outliers on positioning performance. The Institute of Geodesy and Geophysics III (IGG III) function is used for regulation purposes, where prior information is modified and refreshed using the equivalent weight matrix and adaptive factors, thus reducing the impact of system model errors on system state estimation results. In addition, a robust factor is defined to adjust positioning deviation weighting between the pre- and post-test robust estimates. The experimental results show that after robust RTK positioning in the static experiments, the overall improvement in positioning accuracies of the Xiaomi 8, Huawei P40, Huawei mate40, and low-cost M8 receiver reached 29.6%, 31.3%, 32.1%, and 30.7%, respectively. Similarly, after applying the proposed robust method in the dynamic experiments, the overall positioning accuracies of the Xiaomi 8, Huawei P40, Huawei mate40, and the low-cost M8 receiver improved by 28.3%, 32.9%, 35.4%, and 26.2%, respectively. The experimental results reveal that an excellent positioning effect of a smartphone is positively correlated with robust RTK positioning performance. However, it is worth noting that when the positioning accuracy reaches a high level, such as the positioning results achieved using low-cost receivers, the robustness performance shows a relatively decreasing trend. This finding suggests that under the condition of high positioning accuracy, the sensitivity of specific positioning equipment to interference sources may increase, resulting in a decline in the effect of robust RTK positioning.
低成本智能终端的性能受到其低成本全球导航卫星系统（GNSS）硬件和芯片性能的限制，同时也受到复杂城市环境的影响，这会影响GNSS服务的定位精度和稳定性。为此，本文提出了一种适用于不同环境的鲁棒自适应卡尔曼滤波器，该滤波器可在数据预处理后应用。基于卡尔曼滤波算法，将一种鲁棒估计方法引入实时动态（RTK）定位中，以对低成本智能终端的异常观测值进行判断，该方法会放大异常观测方程的方差和协方差，并减少异常值对定位性能的影响。利用大地测量与地球物理研究所III（IGG III）函数进行调节，通过等效权矩阵和自适应因子对先验信息进行修正和更新，从而减少系统模型误差对系统状态估计结果的影响。此外，定义了一个鲁棒因子来调整测试前和测试后鲁棒估计之间的定位偏差权重。实验结果表明，在静态实验中进行鲁棒RTK定位后，小米8、华为P40、华为mate40和低成本M8接收机的定位精度总体提升分别达到29.6%、31.3%、32.1%和30.7%。同样，在动态实验中应用所提出的鲁棒方法后，小米8、华为P40、华为mate40和低成本M8接收机的总体定位精度分别提高了28.3%、32.9%、35.4%和26.2%。实验结果表明，智能手机的优异定位效果与鲁棒RTK定位性能呈正相关。然而，值得注意的是，当定位精度达到较高水平时，如使用低成本接收机获得的定位结果，鲁棒性性能呈现出相对下降的趋势。这一发现表明，在高定位精度条件下，特定定位设备对干扰源的敏感度可能会增加，从而导致鲁棒RTK定位效果下降。
