Applications of the Particle Filter for multi-object tracking and classification

This paper uses several case studies to examine the capabilities of the Particle Filter (PF) for multi-object tracking and classification. The first study treats the relatively simple problem of tracking two objects in the absence of any background clutter. The scenario considers a thrusting ballistic missile that expends a lower stage after burnout. The objective is to track both stages using a single PF which contains separate states for the two objects. The second case study involves a more complicated scenario in which a maneuvering air vehicle deploys a series of decoys to confuse the tracking sensor. The problem is made more complex by the fact that tracking occurs in moderately dense clutter background. The PF state vector includes a discrete binary existence state which denotes whether the decoy is present or absent. In addition to measurements of line-of-sight angles, range, etc., the filter is assumed to have available “classification” which are judgments made by the observer as to the origin of each measurement. The classifications are specified in terms of probabilities. The possible objects under track can be the target alone, a centroid of the target and decoy in the case of unresolved measurements, or separate, resolved tracks of both target and decoy.