Analysis of relative positioning performance of multi configuration fusion for UAV clusters

DOI:	10.11809/bqzbgcxb2023.03.012
Keywords:	UAV cluster; iterative valuation; accuracy factor; extended Kalman filter
Abstract:	For the relative positioning problem of large scale UAV clusters, this paper proposes a non linear iterative solution method based on carrier phase observation through the random aircraft selection strategy.Based on the traditional accuracy factors, an accuracy evaluation principle is derived and constructed. In order to achieve multi configuration fusion solution of UAV state information during the observation period, multiple master aircraft are randomly selected for observation positioning.At the same time, the position information is estimated by using the extended Kalman filter algorithm.The simulation results show that the random aircraft selection strategy achieves multi configuration fusion within the observation period and solves the single configuration limitation.Moreover, an increasing number of master aircraft suppresses the accuracy factors from being too large, which significantly improves positioning accuracy.In addition, the extended Kalman filter algorithm can be used to obtain highly accurate state information, while iterative valuation methods can be used in the pre processing phase before filter estimation, thus further improving location accuracy.This paper provides a means of relative positioning for UAV clusters flying in complex environments.
Published:	2023-03-28
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