Research On Key Technologies Of Multiple Unmanned Aerial Vehicle Time Difference Positioning System

The battlefield environment is highly informationized,real-time,and networked,and being able to detect the opponent’s actions in advance and quickly locate them often dominates the situation on the battlefield.In this context,designing a real-time,covert,and high-precision passive reconnaissance and positioning system is crucial.Adopting a drone platform equipped with passive detection equipment can effectively cope with real-time and ever-changing battlefield environments.However,in complex electromagnetic field environments,there are also challenges to the accuracy requirements of passive positioning systems.In order to improve the positioning accuracy of passive time difference positioning systems based on multiple unmanned aerial vehicles,this article mainly conducts the following work:(1)This paper proposes an improved quadratic correlation delay estimation algorithm based on the HB(Hassab Boucher)weighting function to address the problem of low delay estimation accuracy in time difference positioning systems at low signal-to-noise ratios.The algorithm first preprocesses the signal with quadratic correlation to improve the signal-to-noise ratio,then performs HB weighted function filtering in the frequency domain,and finally sharpens the peak of the correlation function using Hilbert interpolation in the time domain.The experimental results show that the algorithm proposed in this paper outperforms traditional weighted function delay estimation algorithms in different signal-to-noise ratio intervals as a whole,The accuracy of time delay estimation reaches10μs under low signal-to-noise ratio conditions.(2)For the problem of solving the equation system in the time difference localization model,this paper proposes a method based on the Chan algorithm and particle swarm collaborative algorithm.The algorithm first obtains the initial solution through the Chan algorithm,constructs the objective function with the smallest error,and conducts global search through the particle swarm algorithm,gradually approaching the true target solution.This algorithm combines the advantages of Chan algorithm’s simple solution and particle swarm optimization algorithm’s fast iteration speed,overcoming the phenomenon of local convergence in single particle swarm algorithm localization.Experimental results show that the algorithm still has good positioning accuracy under large measurement errors,and its Rate of convergence is better than that of single particle swarm optimization.(3)The influence of other factors other than error on the positioning accuracy of the system is analyzed,including the number of UAVs,station layout,Frame of reference selection,baseline length and baseline angle,and other different spatial layout modes.Based on the analysis conclusion and the specific indicator requirements of the case,a recommended solution is provided.