Box particle filter algorithm， a novel estimation approach based on the combination of sequential Monte Carlo method with interval analysis for processing nonlinear dynamic stochastic systems， is introduced.Within recursive Bayesian estimation theory framework， the core ideas and main principle of box particle filter algorithm are analyzed.The performance characteristics of box particle filter are studied， and the application of the theory is briefly introduced.Finally， several possible research directions of the algorithm in the future are pointed out.