
Radar track prediction method based on BP neural network
Author(s) -
Song Li,
Shengli Wang,
Dingbao Xie
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0655
Subject(s) - track (disk drive) , computer science , artificial neural network , radar , randomness , backpropagation , kalman filter , artificial intelligence , radar tracker , algorithm , telecommunications , mathematics , statistics , operating system
Taking into account the complex electromagnetic environment in which the radar target is located, it is difficult for the traditional track prediction method to adapt for the high complexity, randomness, and uncertainty of the manoeuvring target track. This study proposes a radar track prediction method based on backpropagation (BP) neural network. According to the historical track of the radar target, this method uses BP neural network to model its movement law and obtain the target's predicted track. Finally, the track prediction experiment was conducted with measured data and compared with the Kalman filter track. The results show that the proposed method has higher track prediction accuracy and can be used for radar track prediction.