Research On Tracking Algorithms For Aerial Maneuvering Target

Since the birth of the target tracking theory,with the continuous development and improvement,the target tracking theory has a place.For example,airborne multi target tracking,multi-target early warning,coastal surveillance,maritime traffic control,satellite surveillance and so on.The electromagnetic environment of modern electronic warfare is more and more complex.The need of target tracking is urgent,the test is severe.Based on it,this paper studies the algorithm of maneuvering target tracking in the following aspects.Firstly,this paper discusses the basic theory of target tracking.The coordinate system selection and maneuvering target model are described in detail.Then,the Kalman filter and other methods are described in detail,and their characteristics are compared.In addition,we the filtering divergence is also described.Then,in order to solve the problem of large amount of computation in maneuvering,a single target tracking method based on fuzzy control is proposed.In this paper,we introduce three methods of Kalman filtering,and compare the simulation results,so as to select the method of volume Kalman filter.In this algorithm,the filtering method is incorporated into the interactive multi model,which improves the computational complexity to a certain extent.The Kalman filter method based on single model has good tracking performance and low complexity in the case of non-maneuvering.Based on it,the fuzzy control method is introduced in this algorithm,and the adaptive algorithm is used to switch between the two algorithms.The simulation results show that the proposed method can effectively filter the algorithm in the case of maneuvering and non-maneuvering,which can not only ensure the tracking accuracy,but also reduce the time consuming.Finally,aiming at the problem of high complexity of multi-target tracking algorithm,this paper presents a new method of multi-target tracking based on ellipsoidal threshold.In paper,two methods of probability hypothesis density filtering and probability hypothesis density filtering with probability are introduced.In this algorithm,the Gauss mixed probability hypothesis density filter is adopted.Based on it,this paper introduces the particle clustering and the weightinto ellipsoid threshold.And it can reduce the number of measurements.The simulation results show that the proposed method can effectively reduce the overall time of the algorithm and improve the operation efficiency.