A dynamic resampling particle filter (DRPF), integrated with scattering and migrating operations, is proposed for the flexibility in nonlinear systems subject to changes in the system model without a considerable increase in computational cost. Under the change detection with the tracking error, the scattering and migration are alternatively adopted where the scattering can boost the diversity in the particle population when no change, or slow change occurs, while the migrating updates particles with new observations if the change comes abruptly. Simulation results validate the proposed PF as a promising alternative to the existing PFs. A dynamic resampling particle filter (DRPF), integrated with scattering and migrating operations, is proposed for the flexibility in nonlinear systems subject to changes in the system model without a substantial increase in computational cost. Under the change detection with the tracking error, the scattering and migration are more adopted where the scattering can boost the diversity in the particle population when no change, or slow change occurs, while the migrating updates particles with new observations if the change comes abruptly. Simulation results validate the proposed PF as a promising alternative to the existing PFs.