Radar Data Tracking Using Minimum Spanning Tree-Based Clustering Algorithm
نویسنده
چکیده
This paper discusses a novel approach to associate and re ne aircraft track data from multiple radar sites. The approach provides enhanced aircraft track accuracy and time synchronization that is compatible with modern air tra c management analysis and simulation tools. Unlike existing approaches where the number of aircraft in the radar data must be assumed, this approach requires no such prior knowledge. While commercial aircraft provide ID tags captured in the radar data in the form of Mode 3 transponder codes, general aviation often lacks such transponders, which precludes using the number of codes sensed to count the number of aircraft in the data. To meet this challenge, an approach to track an unknown number of unidenti ed aircraft using a clustering algorithm is proposed. The paper presents a method to relate aircraft between consecutive time frames and re ne the trajectories of those vehicles. Experimental results from evaluating the algorithm and demonstrating its viability are provided.
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