Research On Algorithm For High Maneuvering Target Tracking On Complex Enviroment

Target tracking is the core issue of modern warfare. In the 21 st century, along with a variety of aircraft to emerge with strong mobility, not the general target tracking algorithm but maneuvering target tracking algorithm has been unable to meet demand. Meanwhile,how to keep track of the target is a very important issue in a complex environment. Based on the background above, this paper aimed at :impact on the value of the process noise variance, variable design dimension adjustable white noise model, analysis of the current statistical model, adaptive measurement noise adjustment algorithm, Kalman filter graphical user interface design.The specific research contents are as follows:1) Described the basic concepts and methods to track maneuvering.Tried to explain what "maneuver" is. Sumed up four goals mobility indicators. Introduced linear and nonlinear state estimation methods, and pointed out that Kalman filter loses sensibility for maneuvers when it reach steady state, and introduced strong tracking filter which can fix this problem. Detailed analysis of the characteristics of a high-speed high-strength F22 fighter maneuverability.Enumerated four actual maneuvering trajectories: turn maneuver, dive maneuver, J-turn maneuver, serpentine turn maneuver J turning.2) Studied Several strongly basic maneuvering tracking algorithm.Based on CV, CA and CT model,made a detailed analysis about the process noise and mobility indicators. Proposed a adjustable white noise model with a variation of the structure. The experimental simulation verify its effectiveness. Enumerated the basic method of Sage-Husa and strong tracking filter for the process noise adjustment method. Analyzed the performance of the current statistical model, point out its adaptive tracking model for acceleration.3) Studied maneuvering target tracking algorithm in a complex environment. Introduced the basic sensor fusion algorithms and sequential fusion algorithm. Analysed the improvement of strong tracking filter. The improved strong tracking filter is applied to the adaptive noise measurement process adajust.4) Designed a graphical user interface for the Kalman filter on the Matlab environment.