Robust Direct Position Estimation Algorithm Based on Data Association in Complex Environments

Yueying Zhou, Renbiao Wu, and Qiongqiong Jia

Peer Reviewed

Abstract:	Compared to Scalar Tracking Loop (STL) and Vector Tracking Loops (VTL), Direct Position Estimation (DPE) tightly couples signals from multiple satellite channels and estimates the navigation solution directly from the combined correlation in the navigation domain. As a result, DPE exhibit enhanced robustness in non-ideal signal propagation environments. However, DPE performance can still be impacted by multipath and NLOS interference. To improve DPE robustness under such adverse conditions, this paper proposes a robust direct position estimation algorithm based on data association. In this method, all visible satellites are divided into multiple observation subgroups. For each subgroup, a state estimate is obtained from the combined correlation of signals in the navigation domain. Subgroups exhibiting significant discrepancies between their estimated states and the predicted state are identified as being affected by abnormal propagation and are excluded. The final navigation solution is then derived by performing a weighted combination of the remaining reliable subgroup estimates. Simulation and real-world experimental results validate the effectiveness of the proposed robust direct position estimation algorithm based on data association under multipath and NLOS and further compare its performance with varying numbers of satellites in each subgroup.
Published in:	Proceedings of the 38th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2025) September 8 - 12, 2025 Hilton Baltimore Inner Harbor Baltimore, Maryland
Pages:	1558 - 1569
Cite this article:	Zhou, Yueying, Wu, Renbiao, Jia, Qiongqiong, "Robust Direct Position Estimation Algorithm Based on Data Association in Complex Environments," Proceedings of the 38th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2025), Baltimore, Maryland, September 2025, pp. 1558-1569. https://doi.org/10.33012/2025.20239
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