Research On Data Association Algorithms For Multi-target Fast Tracking

With the continuous development of radar technology,radar target detection and tracking system play an increasingly important role in the national defense.It is necessary to quickly and accurately capture all suspicious targets in the surveillance area and track them in real time.In complex electromagnetic environment,space is full of various clutter and interference signals.This puts forward higher requirements for target tracking technology,and especially brings great challenges to the data association.In this paper,the process of real-time multitarget tracking is studied in depth.The main research contents are summarized as follows:Firstly,the normal nonlinear Kalman filter theories for maneuvering target tracking is studied.Aiming at the slow convergence rate of target tracking due to large measurement error,a new approach of initial estimation for Kalman tracking filter is proposed,which is called projection correction method.If the target initial velocity and acceleration calculated by the three-point method are out of the range,they will be repaired.Then the repaired value are used as the new initial value to filter.Compared to the traditional initialization method,the proposed initialization method is combined with the extended Kalman filter,unscented Kalman filter,and cubature Kalman filter nonlinear algorithm in the simulation.As a result,the initialization method we proposed can significantly improve the prophase convergence efficiency and ensure a certain degree of filtering accuracy.In addition,the study further found that correction with zero-value can get better convergence under the same conditions.Secondly,the real-time tracking of multiple targets which maneuver in parallel neighboring or small-angle crossing scene is further studied.The traditional data association algorithms and the existing feature-aided data association algorithms are introduced and analyzed.On the basis of the idea of fuzzy mathematics,a new method of feature-aided data association based on hesitant fuzzy sets is proposed.It assigns the attributes of controversial public measurements in a fuzzy way.The allocation probability of public measurements is used to repaire the association probability between targets and measurements,which is obtained by probabilistic data association algorithm.And then Kalman filter algorithm is carried out to track the targets.The simulation results show that this method can achieve higher correlation accuracy,and the real-time performance of the algorithm is strong,which is more in line with the needs of engineering practice.Finally,the research on the interactive multi-model target tracking algorithm is carried out to track multiple maneuvering targets,which are close to each other.Establish multiple target tracking filters based on multiple models.At each sampling time,the filter estimates at the previous time are mixed and interacted.And then the new initial conditions of each model are regenerated.Based on these re-initialization results,combined with data association algorithm,Kalman filters are used to estimate and update the probability of each model.At last,the estimated values of each filter are fused to obtain the integrated filter state vector at that time.The simulation results show that the feature-aided data association algorithm based on hesitant fuzzy sets has the highest accuracy for target-measurement correlation.Combining the algorithm with the interactive multi-model target tracking,the overall stability of the tracking system can be greatly improved.