access icon openaccess Radar working-state identification using the hidden Markov model

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 25/02/2019, Accepted 08/05/2019, Published 11/07/2019

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.

Inspec keywords: statistical analysis; hidden Markov models; signal processing; natural language processing; radar signal processing

Other keywords: HMM-based statistical identification method; model parameters; radar state identification approach; intelligent identification; hidden Markov model; radar working-state identification; statistical signal processing principle; natural language processing

Subjects: Markov processes; Speech processing techniques; Signal processing and detection; Natural language interfaces; Markov processes; Radar equipment, systems and applications; Other topics in statistics; Speech recognition and synthesis

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