Research on Formation Precision Positioning Method Based on Main-Slave UAV Collaboration

The custom drone formation positioning technology refers to a technology that combines multiple drones into a formation and achieves real-time acquisition of information such as the position and speed of each drone in the formation through positioning technology, and adjusts the formation accordingly. In this technology, relying on base stations to locate all unmanned opportunities in the drone formation wastes communication resources; And completely detached from the base station, relying solely on several drones in the formation to locate other drones cannot obtain the actual position information of the drones, which is disconnected from reality; At the same time, when the drone cluster is flying in formation, receiving a large amount of electromagnetic wave signals will interfere with the drone formation, which is not conducive to maintaining the formation. Based on this, this article proposes a new unmanned aerial vehicle formation positioning algorithm, which divides the formation positioning process into two stages: main unmanned aerial vehicle positioning and secondary unmanned aerial vehicle positioning. Corresponding sub algorithms are designed for each of the two positioning stages. In the positioning stage of the main drone, we established an air to ground channel model. At the main drone end, based on the received RSS from the base station, we used a three-point positioning method to locate the main drone; During the localization phase of the drone, all drones maintain electromagnetic silence. Based on the directional information received from the drone end from the main drone, the drone's position information obtained during the localization phase is used to locate the drone using a dual circle localization model under the premise of passive localization with pure bearing. Finally, all drones in the drone formation are located, and the formation shape can be adjusted based on the positioning information. This article simulates the entire process and analyzes the impact of the number of signals transmitted by the base station, noise amplitude, and deviation range from the drone on positioning accuracy. It is found that when the noise amplitude is less than 1.27 and the number of signals transmitted by the base station is greater than 7, the relative error of the drone being positioned in the overall system is less than 5%, which verifies the accuracy of the algorithm. When the positioning accuracy requirement is high, the number of signals transmitted by the base station can be increased to reduce the positioning error.