Research On The Cooperative Positioning Method Of UAV Based On The Fusion Of IMU And Wireless Ranging And Direction Finding

It has become an important research direction to achieve continuous,accurate and reliable positioning of UAV systems in the environment where the Global Navigation Satellite System(GNSS)is interfered or rejected.Inertial Measurement Unit(IMU)has the advantages of low cost,low environmental impact and high accuracy in a short time,the positioning based on the wireless signal has the advantages of flexible deployment,low cost,high precision.The multi-source fusion positioning method based on IMU and wireless signals such as Time-of-Arrival(TOA)and Angle-ofArrival(AOA)can meet the requirements of low construction cost,high accuracy and dynamic performance of the cluster systems.This paper mainly studies the co-location problem of UAV system,and designs and implements a localization method based on the fusion of IMU and wireless ranging and direction finding.The research contents and achievements of this paper are as follows:(1)In view of the difficulty of high-precision positioning of UAV systems in GNSS-rejected environments,this paper studies the errors of IMU,TOA and AOA,and establishes a multi-source fusion positioning model based on Extended Kalman Filter(EKF).The verification and analysis are carried out under different error conditions,number of anchors,operating time and other experimental conditions.The results show that the the multi-source fusion localization architecture of TOA and AOA information can reduce the cluster positioning error.Among them,increasing the number of anchors has the most significant effect on improving the positioning accuracy.The AOA error has a greater correlation with the long-running period positioning error,and the TOA error has a greater correlation with the short-running period positioning error.(2)Aiming at the problem of difficult and slow solution of highdimensional objective function in multi-source fusion positioning.This paper proposes a multi-source fusion positioning solution method based on bionic optimization algorithm,which transforms the multi-dimensional positioning function solution problem into an optimization search problem.Combining IMU,TOA and AOA multi-source errors as the objective function,using random and time-varying ideas,innovating the step search method based on the spiral principle,and obtaining better positioning accuracy.And the bionic constraint mechanism based on the principle of falling leaves is innovated,which improved the solution speed without sacrificing the solution accuracy.The feasibility of the algorithm is verified by experiments,and the time-varying effect of ranging and directionfinding errors on the positioning accuracy is analyzed.(3)Aiming at the time-varying characteristics of the influence of TOA and AOA errors on the overall positioning accuracy,an improved adaptive weight algorithm based on the time-varying effects of errors is proposed.The trust degree of TOA and AOA information is dynamically adjusted according to the time-varying characteristics on the basis of optimized fusion.The experimental results show that the weight improvement algorithm significantly reduces the positioning error,and the average accuracy is increased by 30%under various experimental conditions,and the optimization effect for long operating cycles is greater than that for short operating cycles.