Open AccessRadar Working State Recognition Based on Improved HPSO-BP
Author(s) -
Huiqin Li,
Yanling Li,
Xuemei Wang,
Zhe Xu,
Xinli Yin
Publication year - 2021
Publication title -
international journal of antennas and propagation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.282
H-Index - 37
eISSN - 1687-5877
pISSN - 1687-5869
DOI - 10.1155/2021/5586851
Subject(s) - radar , backpropagation , artificial intelligence , artificial neural network , particle swarm optimization , convergence (economics) , computer science , pattern recognition (psychology) , rate of convergence , machine learning , engineering , automatic target recognition , key (lock) , synthetic aperture radar , telecommunications , computer security , economics , economic growth
In this paper, a recognition model based on the improved hybrid particle swarm optimisation (HPSO) optimised backpropagation network (BP) is proposed to improve the efficiency of radar working state recognition. First, the model improves the HPSO algorithm through the nonlinear decreasing inertia weight by adding the deceleration factor and asynchronous learning factor. Then, the BP neural network’s initial weights and thresholds are optimised to overcome the shortcomings of slow convergence rate and falling into local optima. In the simulation experiment, improved HPSO-BP recognition models were established based on the datasets for three radar types, and these models were subsequently compared to other recognition models. The results reveal that the improved HPSO-BP recognition model has better prediction accuracy and convergence rate. The recognition accuracy of different radar types exceeded 97%, which demonstrates the feasibility and generalisation of the model applied to radar working state recognition.
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