A Multi Scan Labeled Random Finite Set Model for Multi Object State Estimation
State space models in which the system state is a finite set called the multi object state have generated considerable interest in recent years. Smoothing for state space models provides better estimation performance than filtering. In multi object state estimation, the multi object filtering density can be efficiently propagated forward in time using an analytic recursion known as the generalized labeled multi Bernoulli (GLMB) recursion. In this paper, we introduce a multi scan version of the GLMB model to accommodate the multi object posterior recursion, and develop efficient numerical algorithms for computing this so called multi scan GLMB posterior.
IEEE Transactions on Signal Processing