Bayesian Track Correlation and Numbering

Abstract

In decentralized tracking a single situation picture is formed by correlating (associating) and merging tracks from different track sources. A problem rarely addressed in this connection is that of maintaining correct track numbers over time. The track numbers may have to be reassigned, for example when the track sources make mistakes like swapping tracks or picking up false measurements. This paper presents a coherent Bayesian approach to handling the dynamics in track correlation, where the basic idea is to consider track-to-target correlation instead of the more conventional track-to-track correlation