A novel motion model and tracking algorithm

A novel motion model and adaptive algorithm for tracking maneuvering target are proposed, in which the acceleration of maneuvering targets is considered as a time-correlation random process with non-zero mean values and the probability density of the acceleration is assumed by Gaussian distribution. The mean value of the distribution function is the optimal estimation of the target acceleration at present and its variance is directly proportional to the square of the differential coefficient of the optimal estimations of the target acceleration at present. The Monte Carlo simulation results show that the model and adaptive algorithm proposed in this paper can estimate the position, velocity and acceleration of a target well and requires less computation than the others, no matter what the target is maneuvering at any form.