Distributed GNSS Collaborative Positioning Algorithms and Performance Analysis

In global navigation satellite system (GNSS) collaborative positioning, each user shares its processed data with the neighboring users and conducts joint data processing. The study of the performance for distributed GNSS collaborative positioning algorithms is essential for the actual applications. In this paper, the Distributed Least Squares (DLS), Distributed Extended Kalman Filter (DEKF) and Distributed Unscented Kalman Filter (DUKF) algorithms are introduced in detail. After that, the simulation tests are carried out to analyze the performance in the outdoor and partially blocked scenarios respectively. The results show that the performance of three distributed algorithms is all better than GNSS standalone Least Squares algorithm, especially in the partially blocked scenario, the performance improvements are significant. Compared with the other two algorithms, the DEKF algorithm is pretty good in terms of performance and computational complexity. Besides, considering that the computational complexity of users is limited, the results also provide useful information of how to select collaborative users for a better positioning accuracy in different scenarios.