Abstract: The existing multiple model hypothesis density filter can estimate the number and state of maneuvering targets at the same time. Yet its Sequential Monte Carlo (SMC) implementation involves clustering algorithm, which is unstable and time consuming, and may result in tracking target loss. To solve the problem, this paper proposes a Multiple Model (MM) Cardinality Balanced Multiple target Multi-Bernoulli (CBMeMBer) filter. When the clutter number of per-scan is less than 20 and detection probability is higher than 0.9, this lgorithm transmits the posterior density of maneuvering targets through a set of time-varying Bernoulli parameters, according to which, the targets state can be computed by simple operations, thus effectively avoids the clustering algorithm. Simulation results shows that compared with multiple model hypothesis density filter, the algorithm proposed decreased the OSPA distance which chooses to estimate tracking errors.