• ISSN 0258-2724
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Volume 31 Issue 1
Jan.  2018
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Article Contents
Journal of Southwest Jiaotong University  > 2018  >  53(1): 189-196.
ZHU Bin, JIN Weidong, YU Zhibin. Intrapulse Feature Evaluation Model of Radar Emitter Signal Based on Differential Evolution, Particle Swarm Optimization and Projection Pursuit Algorithm[J]. Journal of Southwest Jiaotong University, 2018, 53(1): 189-196. doi: 10.3969/j.issn.0258-2724.2018.01.023
Citation: ZHU Bin, JIN Weidong, YU Zhibin. Intrapulse Feature Evaluation Model of Radar Emitter Signal Based on Differential Evolution, Particle Swarm Optimization and Projection Pursuit Algorithm[J]. Journal of Southwest Jiaotong University, 2018, 53(1): 189-196. doi: 10.3969/j.issn.0258-2724.2018.01.023
shu

Intrapulse Feature Evaluation Model of Radar Emitter Signal Based on Differential Evolution, Particle Swarm Optimization and Projection Pursuit Algorithm

doi: 10.3969/j.issn.0258-2724.2018.01.023
  • Received Date: 20 May 2015
  • Publish Date: 25 Feb 2018
    Abstract
  • To address the problems in comprehensive evaluation of radar emitter signal (RES) intrapulse features, such as incomplete evaluation criteria and the lack of objectivity, a new comprehensive evaluation model of RES intrapulse features was proposed based on swarm intelligence. First, the comprehensive evaluation problem of RES intrapulse features was converted into an optimization problem of the conditional multivariate nonlinear objective function through the projection pursuit algorithm. Secondly, the new swarm intelligence algorithm was obtained through the combination of the improved particle swarm optimization algorithm and the differential evolution algorithm. Thirdly, the optimization and solution of the multivariate nonlinear objective function was achieved using the proposed algorithm. The simulation results show that the optimal fitness of the Rosenbrock test function of this new intelligence algorithm is minimal, and the optimal fitness values of the Rastrigrin test function and the Girewank test function are zero, indicating that the calculation accuracy of the proposed algorithm is better than the standard particle swarm optimization algorithm and the differential evolution algorithm. At the same time, the variance of the fitness value of the proposed algorithm is smaller than those of the standard particle swarm optimization algorithm and the differential evolution algorithm, which indicates that the convergence and robustness of the proposed algorithm are better. According to the objective function of the evaluation problem, the results of the proposed algorithm do not fluctuate when comparing the five optimization results with those of the accelerated genetic algorithm, which shows that the intrapulse feature evaluation model based on swarm intelligence can effectively achieve the objective of comprehensive evaluation of RES intrapulse features.

     

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