Multiple-model Bayesian filtering with random finite set observation

The finite set statistics provides a mathematically rigorous single target Bayesian filter(STBF) for tracking a target that generates multiple measurements in a cluttered environment.However,the target maneuvers may lead to the degraded tracking performance and even track loss when using the STBF.The multiple-model technique has been generally considered as the mainstream approach to maneuvering the target tracking.Motivated by the above observations,we propose the multiple-model extension of the original STBF,called MM-STBF,to accommodate the possible target maneuvering behavior.Since the derived MMSTBF involve multiple integrals with no closed form in general,a sequential Monte Carlo implementation(for generic models) and a Gaussian mixture implementation(for linear Gaussian models) are presented.Simulation results show that the proposed MM-STBF outperforms the STBF in terms of root mean squared errors of dynamic state estimates.