# A Novel Error Correction Approach to Improve Standard Point Positioning of Integrated BDS/GPS
## 5 Conclusion  
This paper provides a novel approach to the standard point positioning of integrated BDS/GPS. First, the different error sources of standard point positioning of integrated BDS/GPS are elaborated. Among them, some error sources are difficult to estimate during the positioning process. In some current positioning methods, they are also difficult to obtain very effectively reduced or suppressed. Therefore, an LSTM-based error prediction and correction method is proposed in this paper. On the basis of the traditional positioning method, the data with multiple error sources are learned and predicted, and the prediction results are used for error correction to effectively reduce and suppress the error containing multiple error sources. The four methods, namely weighted least square method, Kalman filter, WLS-LSTM and Kalman-LSTM, were tested and compared based on the measured data. It turns out that the LSTM recurrent neural network-based error correction method has greatly improved the accuracy of standard point positioning. The proposed Kalman-LSTM error correction method achieves the best performance, whose point positioning error can almost reach the sub-meter level under the experimental environment. Furthermore, through the error correction method, the positioning error caused by various factors such as the receiver clock deviation can be effectively suppressed. The greater the error is, the more the  
Figure 16: The CDF and PDF of four different methods: (**a**) CDF, and (**b**) PDF. Figure 17: The CDF and PDF of dynamic error: (**a**) original error; and (**b**) corrected error. Kalman-LSTM can contribute to the positioning improvement of the methods without LSTM error correction. In addition, the experimental results in dynamic scenes also show that the proposed LSTM-based error correction method can effectively improve the positioning accuracy. As for future work, parameter tuning of the LSTM network structure can be developed, and the models in different environments and scenarios can be trained to adapt the method to the navigation and positioning requirements in different environments and scenarios. Conceptualization, L.D. and W.C.; methodology, L.D. ; software, L.D. ; validation, L.D. and Z.P. ; formal analysis, L.D. and J.J.; writing--original draft preparation, L.D. ; writing--review and editing, L.D., W.C. and J.J.; supervision, W.C.; project administration, W.C.; and funding acquisition, W.C. and L.D. All authors have read and agreed to the published version of the manuscript. The research work was supported in part by the National Key R&D Program of China under Grant 2018 YFB0105205, in part by Hubei Province Technological Innovation Major Project under Grant 2018 AAA059 and 2019 AAA025, and in part by the Fundamental Research Funds for the Central Universities (WUT: 2019-JL-023). The authors express their sincere gratitude to [PERSON] from Shanghai Engineering Technology Research Center for Intelligent and Connected Vehicle Terminals for his contribution to the validation of the article. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: