Expectation Maximization (EM) algorithm-based nonlinear target tracking with adaptive state transition matrix and noise covariance

A novel method involved the time-varying tracking model under the nonlinear state-space evolved system is presented, in which the expectation-maximization (EM) algorithm is used to identify the state transition matrix f and the process noise covariance Q online. The typical maneuvering models, as described, essentially, are prior models and use fixed and constant evolved matrix and designed noise level for whole filtering procedure. Actually, the motion of target is always too complicated to be prior modeled as a fixed form, meanwhile, Q used to reflect the mis-match error between the mathematic model and the actual maneuvering mode, thus is time-varying and severely influenced by the environment around the target. Therefore, the prior f and Q can not characterize the maneuvering mode exactly, hence, by assumption that the state evolution and the likelihood of measurements data can be represented by Gaussian distribution, the method of identifying f and Q online is developed. Comparing with the standard IMM filtering, Monte Carlo simulations show that the proposed algorithm is efficient and filtering precision can be improved to some extent.