Multi Object Tracking Optimization Based On Hybrid Particle Swarm Optimization Algorithm For Cyber-Physical System

Cyber-physical system(CPS)is a kind of complex information system which integrates computing,communication and control.Multi target tracking is an important application field of CPS,which involves multi target information acquisition,real-time positioning and tracking prediction.In order to improve the target tracking accuracy in precision positioning and path prediction aspects,this dissertation introduced hybrid particle swarm algorithm in CPS multi target tracking,we designed and realized the optimization of multi target location and path prediction optimization to solve this problem.The thesis firstly designed CPS multi target localization model,and then puts forward a hybrid particle swarm algorithm CPS multi target localization method based on CPS,then we designed a multi target tracking model,finally we proposed the prediction method of hybrid particle swarm algorithm CPS based on multi objective path.Aiming at the optimization scheme of the CPS multi target localization and path prediction,the simulation experiments and performance analysis are carried out.The innovation of this paper is mainly reflected in the following two aspects:(1)The current target locating algorithm of CPS has the problem of slow convergence and easy to fall into local optima in dealing with constrained optimization,we introduced the crossover and mutation strategy in the application of particle swarm algorithm,to avoid CPS target into the local optimum in the iterative process.In addition,the space distance constraint and geometric constraint as CPS multi target locating target function in the model,the hybrid particle swarm algorithm to obtain the CPS optimal solution in multi target tracking,compared with the standard particle swarm algorithm,the reduced CPS target positioning error,and shorten the time to reach the extreme points of the target.(2)The current multi target tracking method in the path of the target prediction accuracy in CPS has the problem of insufficient target precision accuracy,we introduced the sampling and resampling strategy in the application of particle swarm algorithm,the prediction of particle weight updating after normalization,select and copy the larger weight of population again iteration,to avoid the particle degradation phenomenon.The use of the optimized formula for population of particles was observed in different time value,the target particle in CPS continuously at the actual state slowly approached,and solved the particles in the iteration due to particle and reduced the accuracy problem of scarce.