299 / 2023-10-11 18:33:41

Research on the positioning performance of Robust RTK in low-cost GNSS terminals

RTK,GNSS,Adaptive Robust Kalman Filtering

Low-cost GNSS terminals are limited by their low cost and low energy consumption characteristics, and are susceptible to errors in the observation environment, resulting in poor positioning accuracy and stability. Although abnormal data can be processed through data preprocessing, the actual measurement situation is too complex and it is difficult to control the threshold setting, making it impossible for data preprocessing to completely eliminate every outlier. When the number of outliers exceeds half, the method of directly eliminating outlier observations will seriously affect the later positioning results. The processing method of robust Kalman filter is to increase the variance of abnormal observation values, and there is a better solution to this gross error. Therefore, this article performs adaptive robust Kalman filtering under different environments after the data preprocessing stage, introduces robust estimation into RTK positioning, identifies outliers through adaptive robust filtering, and increases the accuracy of the outlier observation equation. Variance and covariance reduce the impact of outliers on positioning performance. Based on the RTK positioning model of non-difference error correction numbers, the adaptive robust Kalman filter algorithm is used to analyze the improvement effect of the positioning performance of robust RTK in different environments. All gross errors in the pre-test residuals and the post-test residuals exceed the threshold. The residuals are subjected to anti-difference processing and measurement updates until the standardized residuals are within the threshold. The anti-collision RTK experiment of measured data on low-cost GNSS terminals shows that: in the anti-collision RTK static experiment of measured data, the anti-collision effect is more obvious. Compared with the conventional RTK method, the positioning accuracy of the robust RTK method in the E, N, and U directions is increased by 35.7%, 30.8%, and 33.9% respectively, the convergence time is increased by 23 seconds, and the fixation rate is increased by 7.8%. Analysis of the robust RTK positioning accuracy in dynamic environments shows that in an open environment, compared with conventional RTK, the robust RTK method improves the positioning accuracy in the E, N, and U directions by 24.2%, 28.1%, and 26.2% respectively. In partial occlusion environments, the positioning accuracy increased by 19.6%, 23.3%, and 20.1%. In an occlusion environment, poor data quality makes the improvement of positioning accuracy less effective. Analysis of the convergence time and fixation rate of robust RTK in dynamic environments shows that in open environments and occlusion environments, compared with conventional RTK, the convergence time of the robust RTK method is improved by 30.7% and 27.9% respectively, and the fixation rate is improved by 30.7% and 27.9% respectively. They increased by 22.9% and 12.3% respectively. The above experimental results show that the worse the quality of the data, the smaller the positioning improvement effect of RTK. The results show that the adaptive algorithm can effectively improve the positioning accuracy of low-cost GNSS terminals, but as the positioning accuracy decreases, the positioning accuracy improvement effect after tolerance will also decrease.