Research On Target Tracking Filtering Method

Target tracking technology has a wide range of applications in military and civilian applications,such as remote warning,battlefield surveillance,and civil aviation transportation.The target tracking technology is a technology that uses the sensor to obtain the relevant information of the tracked target,and uses the relevant filtering algorithm to predict the motion state of the target at the next moment and estimate the current motion state.As the target environment becomes more and more complex,such as the presence of human factors in information warfare,the target tracking technology is also constantly being developed and updated in order to achieve accurate tracking of targets.The tracking of maneuvering targets is a current research.Hot spot.The main work of this paper is as follows:This paper firstly analyzes the basic principle of target tracking.It introduces the commonly used parameter estimation methods,least squares method and minimum mean square error estimation method.Afterwards,it introduces some basic concepts in target tracking,such as related wave gate and track start.,data associations,etc.,and simulations and analysis of track-head related algorithms.Afterwards,the Kalman filtering algorithm in the linear filtering method is introduced in detail.The system model,the filtering model,the initialization problem of the algorithm are analyzed,and the Kalman filtering algorithm is simulated and analyzed.In fact,there is a nonlinear relationship between the sensor measurement and the target motion state.When the nonlinear relationship can be used in the low-order approximation,the extended Kalman filter can achieve better tracking performance,and when the nonlinearity is more serious,A nonlinear filtering algorithm must be used.Therefore,the nonlinear filter algorithm is introduced,including extended Kalman filter algorithm,insensitive Kalman filter algorithm,particle filter algorithm and so on.Secondly,the data interconnection problem is studied.There is clutter and human interference in the target environment.The correct interconnection of data is the precondition for accurate tracking of the target.This paper mainly introduces the probability data interconnection algorithm and joint probability data interconnection algorithm,in which Joint Probabilistic Data Association Algorithm is a data interconnection problem suitable for multi-objectives,and related simulations and analysis are performed on these two algorithms.Because JPDA has a large amount of computation,it is not suitable for application in engineering practice.Afterwards,it briefly introduces the empirical JPDA algorithm.This algorithm is a simplification of the JPDA algorithm,which further improves the computational efficiency.Finally,the maneuvering target tracking problem is studied and analyzed.Maneuvering target tracking is a hot topic in the current target tracking research.The common maneuvering target tracking algorithms include maneuvering detection and no maneuvering detection algorithms.This article mainly introduces the interaction in non-motorized detection algorithms.Multi-model algorithm,including the working principle of the algorithm,and its corresponding simulation and analysis.Since the interactive multi-model algorithm can only be applied to the single-target tracking problem,the author combined the Joint Probabilistic Data Association Algorithm and the Interactive Multiple Model Algorithm in the multi-target tracking algorithm in Chapter 3 to implement multiple maneuvers.The goals were tracked at the same time and related simulations and analyses were done.