Open AccessDeep forest for radar HRRP recognition
Author(s) -
Wang Yanhua,
Bi Xuejie,
Chen Wei,
Li Yang,
Chen Qiao,
Long Teng
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0723
Subject(s) - artificial intelligence , computer science , radar , deep learning , cascade , ensemble forecasting , set (abstract data type) , ensemble learning , pattern recognition (psychology) , layer (electronics) , range (aeronautics) , machine learning , engineering , telecommunications , chemistry , organic chemistry , chemical engineering , programming language , aerospace engineering
High‐resolution range profile (HRRP) has received intensive attention in the radar automatic target recognition filed. Here, deep forest is applied to the recognition of HRRP. The deep forest is a deep learning method, which is a cascade of ensemble learners. In each layer, there are various ensemble learners. The input of each layer is the combination of the previous layer output and the original input data. The number of cascade levels can be adaptively determined such that the model complexity can be automatically set. Experiments based on measured data show that deep forest is feasible and achieves highly competitive performance for HRRP recognition.