Polynomial chaos Kalman filter for target tracking applications

In this paper, an approximate Gaussian state estimator is developed based on generalised polynomial chaos expansion for target tracking applications. Motivated by the fact that calculating conditional moments in filter involves computing integrals with respect to density, authors non-linear dynamics using expansion. Second-order as well third-order expansions were used filtering, derive necessary recursive algorithm and also provide certain algebraic simplifications which reduce computational burden without significantly affecting filtering performance. Two comprehensive numerical experiments multivariate systems, including one a multi-model system, demonstrate potential of new algorithms.