Research On Airport Surface Surveillance Based On Multipoint Positioning Technology

In recent years,the topic of air traffic management has become a hot topic for scholars around the world.Facing the current complex air traffic situation,traditional radar monitoring system often presents problems such as abnormal location data,large location deviation and susceptibility to electromagnetic interference,which greatly limits the development of air traffic and increases people’s travel risks.The multi-point positioning system can detect,locate,track and predict the target,which is the core technical means of modern air traffic management.Whether the layout of the ground base station of the multi-point positioning system is reasonable.Firstly,the base station deployment model is established.Then,using the geometric factor of accuracy(GDOP)as the evaluation index,we discuss the comparison of the wedge-shaped,star-shaped,T-shaped and rhomboid deployment schemes,and then discuss the distribution of the GDOP values of the target system at different flight heights.Finally,a ground base station deployment scheme based on immune optimization algorithm was proposed.Simulation experiments were carried out from different perspectives,which proved that the ground base station deployment scheme based on immune optimization algorithm was optimal,and the optimal deployment scheme of 5,6,7 and 8 ground base stations was given,which made a good preparation for subsequent experiments.For the analytic problem of time difference of arrival(TDOA)equation,three traditional analytic algorithms,namely Chan algorithm,Taylor algorithm and Newton algorithm,are introduced respectively.Then,a Residual weighting method was proposed to improve the performance of Chan algorithm,which was called RWEC-Chan(Residual Weighting Error Correction-Chan)algorithm.Then,an improved algorithm to Taylor algorithm,C-Taylor algorithm,is proposed.Subsequently,a new local algorithm is proposed by combining the Chan algorithm with an improved particle swarm optimization algorithm,which is named LDWPSO-chan(Linear Decreasing Weight Particle Swarm Optimization-Chan)algorithm.Finally,the simulation and comparison experiments are carried out to verify that LDWPSO-chan algorithm has the best comprehensive positioning performance among the six algorithms.In order to solve the problem that the system estimates the trajectory of the target with large deviation,it is necessary to filter the positioning data.Therefore,a new particle filtering algorithm for tracking the trajectory of the target in the multi-point positioning system is proposed.In this algorithm,the importance density function is generated for the PF(Particle Filter Algorithm)by combining the untraced Kalman algorithm with the latest location data to guide the generation of PF sampling particles.In this way,the generated sampling particles are closer to the true value of a posteriori distribution.Then,generalized regression neural network(GRNN)is used to optimize the previous sample,and the processed sample is taken as the sample of particle filter to filter the target tracking trajectory.Finally,the simulation results show that the improved particle filter algorithm has higher filtering accuracy and computation speed.At the same time,compared with the traditional particle filter algorithm,the improved algorithm can make the target location trajectory closer to the real flight trajectory of the target.