Kalman filtering beam tracking recovery method based on beam position scanning

Abstract

The invention discloses a Kalman filtering beam tracking recovery method based on beam position scanning, and belongs to the technical field of wireless communication. According to the invention, a Kalman filtering mode is adopted to calibrate beam pointing according to prior information to achieve matching, and rapid and accurate beam tracking is realized. Compared with a traditional mode, the method provided by the invention has the advantages that the required communication traffic is smaller, the consumed computing resources are fewer, and the tracking recovery speed is higher, so that the reliability of beam tracking in a network dynamic environment is improved, and the loss caused by occasional tracking failure is reduced.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and particularly refers to a Kalman filter beam tracking recovery method based on wave position scanning, which is suitable for realizing the self-organizing network process of an unmanned aerial vehicle by using a directional antenna. Background technique [0002] UAV self-organizing network has gained widespread attention due to its advantages of low cost, high flexibility, and the ability to perform complex tasks collaboratively. However, in addition to the dynamic nature of the general UAV ad hoc network, the large-scale and high-dynamic UAV ad hoc network usually has a distance of hundreds of kilometers between nodes, which is difficult for the establishment of communication links and communication Quality assurance poses difficulties. In recent years, equipping UAVs with directional antennas is considered to be an important means to solve the long-distance communication of U...

AI Technical Summary

Problems solved by technology

However, when conducting long-distance directional communication between UAVs, due to the high dynamics of UAV movement, it brings great difficulties to beam alignment

At present, there are some beam tracking methods, such as: traditional tracking based on the least squares method, the positioning accuracy of this algorithm is not high, and the node communication traffic is large; beam tracking based on GPS, Beidou and other navigation systems for positioning is easy to deploy implementation, but the real-time performance of the tracking is poor, and it is often not applicable in the denial environment; although the beam tracking realized by machine vision technology has high precision, even if its huge calculation, energy consumption and delay overhead are not considered, its It is also not practical in long-distance and large-scale communication networks

On the other hand, when the UAV network encounters strong interference or the UAV fails, it will lead to beam tracking failure. At this time, it mainly relies on rescanning to find the target node. However, the adjacency based on beam space scanning The point finding algorithm will scan the entire airspace, which is not efficient

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