Maneuvering target tracking by adaptive statistics model

A good model can extract useful information about the target's state from observations effectively. There are many models used to tracking a, maneuvering target such as constant-velocity (CV) model, Singer acceleration model (zero-mean first-order Markov model) and current model (mean-adaptive acceleration model), etc. While due to the complexity of maneuvering target, to seek the target model which can get better performance is still a subject worthy of study. Based on statistics relation between the autocorrelation function and the covariance of Markov random processing, this paper develops a model which can adaptively adjust system parameters on line. Simulations show the good estimation performance get by the model developed here, and comparing CV, Singer and current models, the model can adaptively get the model parameter while tracking the trajectory and needn't doing several tests to obtain a priori parameter.