A variant of particle filtering using historic datasets for tracking complex geospatial phenomena

The paper presents an extension of the particle filtering algorithm that is applicable when an accurate state prediction model cannot be specified but a database of prior state evolution tracks is available. The conventional particle filtering algorithm represents the belief state as a collection of particles, where each particle is a sample from the state space. The particles are updated by applying the state space equations. In the proposed approach, each particle is an instance of a complete state trajectory, drawn from the database of historic state trajectories. An explicit state update model is not required as the trajectory represented by each particle is covers the entire modeling time period. When new observations become available, a proportion of the particles are replaced using trajectories from the database, selected based on distance from the observation. This tracking algorithm is applicable where the state evolves in a complex manner as in the eye of tropical cyclones. The proposed technique is evaluated by tracking selected cyclones from 2005 using a database of cyclone tracks from the previous 25 years.