This paper is a condensed version of [1]. 2 Nonlinear Bayesian Tracking To de ne the problem of tracking, consider the evolution of the state sequence fx k ; k 2 Ng of a target, given by x k = f k (x k 1 ; v k 1 ); (1) < nv is a possibly nonlinear function of the state x k 1 , fv k 1 ; k 2 Ng is an i.i.d process noise sequence, n x ; n v are dimensions of the state and process noise vectors, respectively and N is the set of natural numbers. The objective of tracking is to recursively estimate x k from measurements z k = h k (x k ; n k ); (2) where h k : < < nn nz is a possibly nonlinear function, fn k ; k 2 Ng is an i.i.d measurement noise sequence, and n z ; nn are dimensions of the measurement and measurement noise vectors, respectively. In particular, we seek ltered estimates of x k based on the set of all available measurements z 1:k = fz i ; i = 1; : : : ; kg up to time k
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