A Two-Stage Approach to Multi-Sensor Temporal Data Fusion

This paper proposes a two-stage architecture for multi-sensor temporal data fusion. The first stage uses extended Kalman filters to track tokens seen by each sensor, and the second stage links the tokens corresponding to the same real-world event. Two pairs of strategies are presented relating to the initial data association between tokens and filters, together with decision rules for switching between them. One pair is for the 'bootstrap' phase and the 'continuous' phase for an event, and the other distinguishes between a complex tracking task and a simpler one. The application of the techniques to a driver's assistant system is described.