Simulation Experiment and Analysis of GNSS/INS/LEO/5G Integrated Navigation Based on Federated Filtering Algorithm

Abstract

This article examines the positioning effect of integrated navigation after adding an LEO constellation signal source and a 5G ranging signal source in the context of China’s new infrastructure construction. The tightly coupled Kalman federal filters are used as the algorithm framework. Each signal source required for integrated navigation is simulated in this article. At the same time, by limiting the range of the azimuth angle and visible height angle, different experimental scenes are simulated to verify the contribution of the new signal source to the traditional satellite navigation, and the positioning results are analyzed. Finally, the article compares the distribution of different federal filtering information factors and reveals the method of assigning information factors when combining navigation with sensors with different precision. The experimental results show that the addition of LEO constellation and 5G ranging signals improves the positioning accuracy of the original INS/GNSS by an order of magnitude and ensures a high degree of positioning continuity. Moreover, the experiment shows that the federated filtering algorithm can adapt to the combined navigation mode in different scenarios by combining different precision sensors for navigation positioning.

Simulations of Dedicated LEO-PNT Systems for Precise Point Positioning: Methodology, Parameter Analysis, and Accuracy Evaluation

“…In this regard, most of the current simulations have focused on fragmented topics of the fullchain LEO-PNT solution [9]. They have focused on how to define new orbit models embedded to the broadcast messages [13], [14], [15], orbit determination [16], [17], [18], signal structure designs [19], [20], [21], atmospheric models [22], [23], [24], constellation optimization strategies to improve the GNSS convergence time [6], [25], satellite clocks [26], [27], user performance estimation [28], [29], [30], [31], [32], [33], [34], and integration with distinct sensors [35]. To expand the simulations, there is still a gap in the process of implementing an end-to-end LEO-PNT system, binding all segments in a unified model.…”

Improved Adaptive Federated Kalman Filtering for INS/GNSS/VNS Integrated Navigation Algorithm

“…However, as the dimension of the state vector increases, the consumption of computational resources increases, and real-time performance deteriorates. Carlson et al [14,15] proposed a federated Kalman filter [16,17] (FKF), which utilizes the principle of information distribution, not only reducing the computational load but also improving the fault-tolerant performance of integrated navigation. In an FKF, all sub-filters evenly distribute the information sharing factor (ISF) [11,18,19] for information sharing.…”

Performance research of real-time kinematic/5G combined positioning model

“…The results showed that the horizontal positioning accuracy and availability of the combined GNSS/5G positioning were considerably higher than those of the GNSS-only network solutions. Wang et al [40] simulated signal sources, such as the GNSS, 5G, and four occlusion scenarios, and performed combined navigation experiments using the Kalman Federation Filter algorithm. The results of the four simulated occlusion scenarios exhibited that the introduction of 5G ranging signals could greatly improve the overall positioning continuity of the Federation Filter.…”