Multi-Sensor Data Fusion Based on ADS-B and MLAT in Approach

ADS-B (Automatic Dependent Surveillance - Broadcast) and MLAT (Multilateration) are the main surveillance techniques for ATC (Air Traffic Control), and will play an important role in the future's tracking system. The fusion between ADS-B and MLAT is able to achieve more accurate tracking. In views of the data characters of these two techniques, this paper designs a concrete fusion framework with multi-levels based on federal Kalman filters to fuse ADS-B and MLAT in approach. Under this framework, both ADS-B and MLAT data are processed intensively to achieve high accuracy. Experimental results based on simulation and practical data illustrate the algorithm can achieve high precision.