Abstract

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.

Highlights

  • Radar working state recognition is used in developing models of the reconnaissance pulse signal characteristics and estimating the internal working state of a radar with prior knowledge. e rapid and accurate recognition of the radar working state is important for determining the radar threat level, evaluating the radar interference effect, and realising the interference decision.When the radar is in a different working state, the signal parameter characteristic exhibits an obvious change. e radar working state recognition can be summarised as a pattern-classification problem. e existing research methods can be summarised as follows: methods based on statistical decisions, methods based on ambiguity decisions, methods based on syntactic structures, and methods based on artificial intelligence [1]

  • These studies did not consider the influence of unstable factors, such as inaccurate prior information and poor signal data, which resulted in poor fault tolerance and generalisation ability. us, the improvement of the efficiency of radar working state recognition requires further investigation

  • Considering the problems of the traditional particle swarm optimisation (PSO) algorithm, this paper proposes the improved HPSO algorithm based on the following three aspects: (1) Crossover and mutation operations in the genetic algorithm (GA) are introduced to update the particles, the hybrid population information is used to enhance the population diversity, and the algorithm’s convergence speed and accuracy are improved

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Summary

Introduction

Radar working state recognition is used in developing models of the reconnaissance pulse signal characteristics and estimating the internal working state of a radar with prior knowledge. e rapid and accurate recognition of the radar working state is important for determining the radar threat level, evaluating the radar interference effect, and realising the interference decision. Some studies [6,7,8] have proposed a syntactic model for accurately extracting radar words from an intercepted radar pulse train for multifunctional radar state recognition These studies did not consider the influence of unstable factors, such as inaccurate prior information and poor signal data, which resulted in poor fault tolerance and generalisation ability. (1) Crossover and mutation operations in the genetic algorithm (GA) are introduced to update the particles, the hybrid population information is used to enhance the population diversity, and the algorithm’s convergence speed and accuracy are improved (2) Improved nonlinear decreasing inertia weights are used to balance the development and exploration of the algorithm (3) An asynchronous learning factor update strategy is used to achieve stronger global search capabilities and faster convergence is study considered the multifunctional phased array radar to investigate the quick and accurate identification of the radar’s working state.

Improved HPSO Algorithm
Recognition of Radar Working State
Model Parameters’ Settings
Analysis of Results
Findings
Conclusions
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