Research Of Track Association System Based On Multi-Hypothesis Tracking

Multi-object tracking technology is a technique that uses sensors to simultaneously track the motion state and trajectory of multiple targets within their observation range.It is widely used for observing and tracking targets in various scenarios such as land,sea,and air.With the development of society and advances in technology,sensor accuracy has improved,while interference during detection has also increased,and complex scenarios demand greater accuracy of target tracking.Currently,the field of multi-object tracking still faces many challenging problems,such as inadequate application in cluttered environments and errors in correlation when trajectories intersect or overlap.Effectively solving these problems and improving the accuracy and robustness of multi-object tracking remains a hotspot and difficulty in current research.Thesis mainly focuses on target prediction and association algorithms in multi-object tracking systems.Based on traditional multi-object tracking systems,the multihypothesis tracking algorithm and association comprehensive evaluation method are analyzed and studied in depth.A simulation data generation platform is developed,target motion models are constructed,and a multi-object track correlation system is designed using the multi-hypothesis tracking algorithm and covariance intersection fusion algorithm.An evaluation method is also extended.The main contributions of thesis are as follows:1.Thesis begins with a brief introduction to the background and practical significance of research on multi-object tracking systems.A comprehensive survey of the research findings of both domestic and foreign scholars on multi-object tracking methods is also presented.The relevant important theories in the field of multi-object tracking are summarized,including target motion models,Kalman filtering,track association algorithms,track fusion algorithms,and comprehensive evaluation methods.2.Due to the difficulty in obtaining measured data of radar tracking targets,a radar simulation data generation platform is established to generate reasonable and close-toreal simulated radar measurement data.The platform supports various types of parameter settings and provides a GUI interface for users to interactively observe the motion trajectory of the radar and set the position of the target for subsequent use by the track association algorithm.3.Using the simulation platform to generate data,simulations are conducted in a single radar multi-object scene and a multi-radar single-object scene.The global nearest neighbor algorithm,joint probability data association algorithm,and multi-hypothesis tracking algorithm are experimentally studied,and the advantages of the multi-hypothesis tracking algorithm are demonstrated in terms of tracking trajectory and mean square error.The covariance intersection fusion algorithm is used for track fusion.4.In response to the current problem of a single evaluation criterion for correlation performance,thesis propose to use the fuzzy comprehensive evaluation method to comprehensively evaluate the trajectory correlation process from multiple dimensions with multiple evaluation indicators,which can better evaluate the advantages and disadvantages of various trajectory correlation algorithms.