The invention discloses an ensemble kalman filter-based particle filtering method, which comprises the following steps of: the initialization of sampled particles, the prediction of a background ensemble at a k moment, the calculation of a kalman gain, the fusion of the latest observation information, the updating of the background ensemble, the recalculation and analysis of the ensemble, the construction of a suggested Gaussian distribution function, the normalization of weights, and the like. The ensemble kalman filter-based particle filtering method provided by the invention avoids the calculation of a Jacobian matrix because a nonlinear system is not required to be linearized, and improves the calculation accuracy by adopting a sampling method to realize approximately linear distribution. In the particle filtering method, a sampling point number is heuristic and can be flexibly set, so the amount of calculation is not increased sharply with the increasing of dimensionality, and the real-time performance is effectively controlled.