Multiple Sensor Tracking with Retrospective Probabilistic Data Association

Abstract

A probabilistic data association approach is described for tracking multiple targets with multiple sensor. This approach employs multiple frames of data in the data association processing. The approach offers improved performance over Joint Probabilistic Data Association tracking. This improved performance is obtained, however, at the expense of increased processing load. In the algorithm is a design parameter that can be selected to adjust performance to suit a specific application. The algorithm is retrospective in that as each new frame of sensor data becomes available earlier tracks are modified and the changes have an impact on subsequent tracks.