Track-to-track data fusion for Unmanned Traffic Management System

Abstract

The growing need of large scale sensor networks for tracking of Unmanned Aerial Systems (UAS) results in high demand of effective algorithms which will provide automatic data fusion and produce human readable results. This paper presents a track-to-track data fusion system, where data from two independent sources are used to track Unmanned Aerial Vehicles (UAVs). The first source of data is Cooperative Surveillance System (CSS) trackers for UAVs, and the second source is Independent Non-cooperative Surveillance (INCS) from a ground based staring radar. The paper provides details on the whole process including: data pre-processing, association, analysis, fusion and output processing. Metrics and their influence on tracking results are also explained. Results of track-to-track data fusion of real-life experimental flight tests are provided.