Random forest-based track initiation method

Abstract

In this study, a novel method based on the random forest is presented to solve the problem of track initiation in the air-traffic-control (ATC) radar system. ATC radar is the most common civilian surveillance radar. There are dense targets with different moving models in its observation area. When implementing track initiation, previous heuristic methods often fail to meet its high requirements for target detection rate and false track suppression. In the method proposed in this study, the general track initiation problem is modelled as a two-category classification problem of measurement sequences. From the historical data, the sample set is constructed. The classification function is derived by Breiman's random forest. Simulation experiments and measurement experiments show that the proposed method outperforms conventional heuristic methods in the scene of the ATC system.

Keywords

• search radar
• target tracking
• radar detection
• radar tracking
• signal classification
• air traffic control
• random forests
• Breiman random forest
• measurement sequences
• observation area
• civilian surveillance radar
• two-category classification problem
• false track suppression
• target detection rate
• air-traffic-control radar system
• random forest-based track initiation method