Most multitarget tracking filters assume that one target and its observation follow a hidden Markov chain (HMC) model, but the implicit independence assumption of the HMC model is invalid in many practical applications, and a pairwise Markov chain (PMC) model is more universally suitable than the traditional HMC model. The existing Gauss mixture implementation of cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter based on the PMC model is only applicable to the linear Gauss system.Each hypothetical path of the Bernoulli random finite set is approximated by a set of weighted particles, and then the sequential Monte-Carlo(SMC) implementation of the PMC-CBMeMBer filter is proposed for nonlinear systems. The experimental results show that SMC-PMC-CBMeMBer filter has better tracking performance than the SMC-HMC-CBMeMBer filter and the SMC-PMC-PHD filter.