Joint optimization algorithm of estimation and identification for reentry target tracking

Reliable identification of ballistic coefficient and accurate estimation of target state are important issues and coupled: the state estimation error may trigger identification risk while identification risk causes state estimation error due to modeling mismatch. Therefore, it is essential to estimate the target state and identify unknown model parameters jointly. In this paper, the joint optimization algorithm PF-EM is proposed for tracking a reentry target with unknown ballistic coefficient, which is realized by using particle filter (PF) smoother under the expectation-maximization (EM) iterative framework. In the E-step, the random particle sampling strategy is utilized to approximate the likelihood function to deal with the inherited nonlinearity. In the M-step, the numerical optimization algorithm is applied to update mass-to-drag ratio. In the simulation compared with the traditional algorithm which augments the state vector with the unknown parameter, the proposed algorithm shows the improvement in both state estimate and parameter identification.