Research On The Method Of Mutilateration System

With the rapid development of the aviation industry,the air traffic flow significantly increases,so it is very important to ensure the flight safety in the airspace.As a key link to ensure flight safety in the airspace,surveillance technology has been constantly improving and developing.As a new generation of surveillance technology vigorously promoted by ICAO,multilateration(MLAT)is the key to the realization of advanced scene activity guidance control system.It can realize the full coverage surveillance of terminal area,approach terminal and flight route,effectively improve the precision of surveillance,and realize the efficient surveillance of Civil Aviation management.Therefore,it is of great significance to study the key steps in MLAT.MLAT is a typical distributed system that can be compatible with a variety of data links.It relies on the principle of multiple ground receiving stations arranged on the ground and time-of-arrival(TOA)when receiving the target response signal at the same time.Afterwards,the central processing subsystem is used for data processing to form multiple sets of time-difference-of-arrival(TDOA),to ensure accurate time synchronization between each ground base station and complete the calculation of the target position by calculation.The performance of monitoring system has a strong dependence on the layout of ground base stations,because the extraction of TDOA information must use dispersed ground base stations to simultaneously capture the data chain carrying the target signal,so the layout between ground base stations needs to be designed reasonably.The geometric layout factor is used to describe the positional relationship between the ground stations.Geometric Dilution of Precision(GDOP)is used to measure the geometric layout precision of base station.Discuss GDOP simulation at four different layout stations,then,for the cross-base station flight of the target in the actual positioning process,the immune algorithm is used to complete the optimal combination of sites,and the effectiveness of the algorithm is verified by comparison and simulation.The solution of the target position is obtained from the system of equations with overdetermined nonlinearity and multiple noises,but it is difficult to solve directly from it.In view of the difficulty in solving the target position,the Chan algorithm and the Taylor positioning algorithm are adopted for the simulation analysis.Both algorithms can achievegood results,but the fuzzy solution and the initial guess value are difficult to determine.Therefore,an improved particle swarm optimization(PSO)algorithm is proposed.Simulation results show that the algorithm has fast convergence speed and strong optimization ability.As for the moving target,it is necessary to analyze and describe the movement trajectory of the target,fit the movement curve of the target in the monitoring area,and realize the tracking of the moving target.Aiming at the problem of particle shortage and complicated calculation when particle filter algorithm is used for target tracking,the improved firefly algorithm is adopted to optimize the particle filter algorithm(FA-PF),which makes the fusion algorithm achieve good tracking performance.