New Technique In Tracking Tighly Maneuvering Targets Based On Jerk Model

Target tracking whether in military or civilian fields has been used widely. For instance in air defense and in air traffic control.With the rapid development of aerospace technology,all flight vehicles'speed and maneuverability are higher and higher. Under this background, how to enhance the performance of tracking of high speed and high maneuvering target has become a more important issue. The maneuvering target can worse the performance of the tracking systems ,because of the complexity ,the randomness and the multiplicity of maneuvering,it has a higher technical difficulty whether in the theory and practice for maneuvering targets tracking. Therefore, this paper based on the achievements summed up by our predecessors, focused on the study of high maneuvering target tracking problem.The development procedure of maneuvering target is first reviewed in paper, the theoretical knowledge of the target tracking is introduced in detail: parameter estimation and state estimation, Kalman filter, and discussed the problems in Kalman filtering. On this basis, the related issues are researched. Firstly, the Kalman filter needs to know the model of the state noise and observation noise, however, the aim of the prior statistical information which is rare for describing accurately. At this point, the prior statistic characteristics of personal supposed moving object is easy to lead to personal errors for object tracking. Second, in moving target, object model of the maneuver frequency values are generally based on experience, it also will lead to personal error.Taking these two issues, this paper presents the model based on Jerk Maneuvering Target Tracking Algorithm:1.The first to use System Identification Parameter Identification of relevant knowledge, combined with Jerk Model to mention apart from motor frequency identification method, and because the presence of observation noise, making identification of the non-bias can not meet, so the proposed identification algorithm is a reasonable compensation. Experimental results show that the method can target motion model in the mobile frequency sound recognition.2.Knowledge according to the system identification, parameters, the system can also be viewed as a state, so the motor frequency as the system state equation of a state model was proposed at the same time due to the presence of non-linear, combined with non-filtered Minkaerman processing, through a large number of experiments show that the model and the corresponding filtering algorithm can improve the tracking accuracy of maneuvering targets...