This chapter presents a new approach combining the Bayesian framework with interval methods. When the system dynamics and measurement models have interval types of uncertainties, instead of point state estimates, guaranteed (interval) estimation is a promising approach. First, fundamental concepts from the interval analysis are introduced. Next, a Box Particle Filter (Box-PF) is presented and its theoretical derivation is given based on a mixture of uniform probability density functions. The efficiency of the Box-PF is significant compared with the generic sampling importance resampling particle Filter (SIR PF). With few particles the Box-PF can achieve the same estimation accuracy that the SIR PF achieves with thousands of particles. The performance of the proposed Box-PF is studied and results over examples both with simulated and real data are presented.