The Central Difference Multi-target Multi-Bernoulli filtering algorithms

We present a new multi-target tracking algorithm for nonlinear models, termed as the Central Difference Multi-target Multi-Bernoulli (CD-MeMBer) filter. Sterling's polynomial interpolation formula is used in deriving the filter under the assumption that state and measurement noises are Gaussian and each probability density during the predict and update recursion is approximated by a Gaussian sum. Furthermore, the proposed CD-MeMBer filter was generalized to nonlinear non-Gaussian models, called as the generalized CD-MeMBer (GCD-MeMBer) filter, where the state and measurement noises are approximated by Gaussian sums. The simulation results of the target tracking verify the effectiveness of the proposed algorithm. © 2011 Asian Network for Scientific Information.