Open AccessRandom forest‐based track initiation method
Author(s) -
Liu Shuo,
Li Hongbo,
Zhang Yun,
Zou Bin,
Zhao Jian
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0180
Subject(s) - track (disk drive) , radar , computer science , random forest , heuristic , set (abstract data type) , track before detect , radar systems , function (biology) , data mining , radar tracker , artificial intelligence , telecommunications , evolutionary biology , biology , programming language , operating system
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.