Open AccessRadar working‐state identification using the hidden Markov model
Author(s) -
Zhang Wei,
Gao Youbing,
Zheng Kun
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0649
Subject(s) - hidden markov model , radar , identification (biology) , computer science , markov model , state (computer science) , artificial intelligence , field (mathematics) , markov chain , radar systems , pattern recognition (psychology) , speech recognition , algorithm , machine learning , mathematics , telecommunications , botany , biology , pure mathematics
Using the statistical signal processing principle in the field of natural language processing, a radar state identification approach based on the hidden Markov model (HMM) is proposed. Since each radar state is modelled by three model parameters of a HMM, the radar state identification can be solved from the solution of the evaluation problem of a HMM. Simulation results show that the HMM‐based statistical identification method has tolerance to parameter error, which is suitable for the intelligent identification of the radar state in a complex environment.