Abstract
For unmanned air vehicles (UAVs) to survive hostile operational environments, it is always preferable to utilize all wireless positioning sources available to fuse a robust position. While belief propagation is a well-established method for all source data fusion, it is not an easy job to handle all the mathematics therein. In this work, a comprehensive mathematical framework for belief propagation-based all-source positioning of UAVs is developed, taking wireless sources including Global Navigation Satellite Systems (GNSS) space vehicles, peer UAVs, ground control stations, and signal of opportunities. Based on the mathematical framework, a positioning algorithm named Belief propagation-based Opportunistic Positioning of UAVs (BOPU) is proposed, with an unscented particle filter for Bayesian approximation. The robustness of the proposed BOPU is evaluated by a fictitious scenario that a group of formation flying UAVs encounter GNSS countermeasures en route. Four different configurations of measurements availability are simulated. The results show that the performance of BOPU varies only slightly with different measurements availability.
Highlights
Unmanned air vehicles (UAVs) require an accurate estimate of their positions, velocities, and attitudes in order to control themselves, navigate, and reason about their environment
While most current UAV navigation systems rely on a combination of the Global Navigation Satellite Systems (GNSS) and an inertial measurement unit (IMU), there is a trend towards the use of all navigation sources available to meet the endless pursuit of navigation robustness in the face of new threats and mission challenges [1,2,3]
For the accuracy performance of belief propagation with particle filters over Kalman filters and cooperative least square algorithms has already been proved by existing work such as [28,29], we focused on evaluating the robustness of Belief propagationbased Opportunistic Positioning of UAVs (BOPU)
Summary
Unmanned air vehicles (UAVs) require an accurate estimate of their positions, velocities, and attitudes in order to control themselves, navigate, and reason about their environment. In cooperative positioning [13,14,15,16,17,18,19,20], nodes have pseudoranges from navigation satellites and ranging information with wireless peers. Existing algorithms such as iterative least square and Kalman filters can be extended to cooperative positioning, which leads to cooperative least square and cooperative Kalman filtering algorithm [21]. The positioning sources include (1) pseudoranges and carrier phases from GNSS satellites, (2) ranges and closed-loop Doppler from peer UAVs, (3) ranging information and closed-loop Doppler with ground control stations [32], and (4) time difference of arrival (TDoA) from the signal of opportunities of background wireless infrastructure.
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