Identification of Shooter Model Using Maximum Likelihood Estimation and Hybrid Gradient Optimization

doi:10.3969/j.issn.1000-1093.2018.12.014

Abstract

Abstract: The response of the shooter to the photoelectric display and his control behavior during fiber optical guidance have direct effect on the guidance performance of missile. The maximum likelihood estimation method is used in the identification of shooter model. For the nonlinear optimization in the identification process, a hybrid optimization strategy, which is combined of genetic algorithm and Gauss-Newton optimization, is used to increase the probability of finding the global optimal solution, and the robustness of strategy is enhanced with simplex method. An accurate model for seeker control based on crossover principle is proposed, a simulator is designed to perform multiple human-in-the-loop experiments, and the maximum likelihood estimation is successfully applied to the test data in terms of output error. The results shows that the hybrid optimization algorithm can be used to find the global optimum, and the accurate estimates of shooter model can be obtained. Key

Key words: fiber-opticguidanceweapon, shootermodel, crossovermodel, maximumlikelihoodestimation, hybridgradientoptimization, outputerrormethod, geneticalgorithm, Gauss-Newtonoptimization

CLC Number:

TJ765.2⁺39

WU Jun-xiong, LIN De-fu, WANG Hui, YUAN Yi-fang. Identification of Shooter Model Using Maximum Likelihood Estimation and Hybrid Gradient Optimization[J]. Acta Armamentarii, 2018, 39(12): 2399-2409.

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第39卷第12期
2018 年12月兵工学报ACTA
ARMAMENTARIIVol.39No.12Dec. 2018

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