Research On Track Correlation And Fusion Technology In Composite Tracking

Cooperative Engagement Capability(CEC)is a modern combat method utilizing information technology,in which each combat platform runs the corresponding target tracking algorithm to form its own local track.At the same time,under the function of composite tracking,each platform can also share radar measurement data with each other,and perform track association and fusion estimation to form a composite track with longer tracking and higher accuracy.When the sensors of any platform can not work or its performance is degraded so that the target track can not be tracked and updated,they can form their own situation picture by the information tracked by other combat units.Therefore,it is necessary to get accurate composite track.Information fusion technology plays a key role in this,which expands the spatial coverage and provides accurate fusion track for composite tracking.And judging whether the tracks from different sub-systems come from the same target is the core technology in information fusion.Only with the correct track association can the information fusion get the correct results.Therefore,based on the background of composite tracking in CEC,this thesis mainly studies as follows:1.The multi-platform composite tracking is based on the single-platform target tracking.For single-platform target tracking,basic theories such as target motion model,data association method and state estimation are introduced.The Probabilistic Data Association algorithm and the Joint Probabilistic Data Association algorithm are simulated and analyzed,and the effectiveness of the Kalman Filter algorithm is verified.2.For the problem of multi-radar track association in composite tracking,data preprocessing techniques are introduced,including time alignment and space alignment.The basic theories of track association methods based on statistics and fuzzy mathematics are studied,and the common problems and measures of traditional track association methods are analyzed.In view of the shortcomings of the accuracy and stability of track association,the Grey Wolf Optimization(GWO)algorithm with better performance is introduced.GWO algorithm has the advantages of simple calculation,flexible application and good local optimization performance.GWO algorithm is improved by adding crossover and mutation operators,and a track association method based on the improved GWO algorithm is proposed.The improved GWO algorithm is organically combined with the track association model,which greatly improves the global search ability.Simulation analysis is carried out under the moving environment of two crossed targets and multiple maneuvering targets.Compared with the traditional algorithm,the new algorithm improves the accuracy of association and can perform stable under the environment of both sparse and dense targets.3.In the composite tracking system,the accuracy of target tracking on each platform affects the performance of the fusion track,and the performance of the fusion track affects the performance of composite tracking in combat system.Therefore,it is necessary to study the track fusion estimation in the composite tracking.The traditional track fusion architecture is introduced,and their advantages,disadvantages and adaptive environment is analyzed.Aiming at the characteristics of composite tracking requiring precise strikes,the architecture of multiple fusion centers is given.The appropriate fusion center can be dynamically selected according to the needs,which is conducive to improving the capability of cooperative operations.The two fusion algorithms of Simple Convex Combination and Covariance Weighting are studied,and the effectiveness of the research method is proved by the fusion simulation experiments of the maneuvering target track.