基于最大似然估计和混合梯度优化的射手模型辨识

doi:10.3969/j.issn.1000-1093.2018.12.014

兵工学报 ›› 2018, Vol. 39 ›› Issue (12): 2399-2409.doi: 10.3969/j.issn.1000-1093.2018.12.014

基于最大似然估计和混合梯度优化的射手模型辨识

吴骏雄¹，林德福¹，王辉¹，袁亦方²

(1.北京理工大学无人机自主控制技术北京市重点实验室, 北京 100081； 2.北京特种机电研究所, 北京 100012)

收稿日期:2018-03-28 修回日期:2018-03-28 上线日期:2019-01-31
作者简介:吴骏雄（1991—），男，博士研究生。 E-mail: wjx@bit.edu.cn
基金资助:
北京理工大学基础研究基金项目（20150142009）；中国兵器红箭创新基金项目（20172014060）

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

WU Jun-xiong¹, LIN De-fu¹, WANG Hui¹, YUAN Yi-fang²

(1.Beijing Key Laboratory of UAV Autonomous Control, Beijing Institute of Technology, Beijing 100081, China; 2.Beijing Institute of Special Electromechanical Technology, Beijing 100012, China)

Received:2018-03-28 Revised:2018-03-28 Online:2019-01-31

摘要/Abstract

摘要： 光纤制导过程中射手对于光电显示的响应及其控制行为将直接影响弹药的制导控制性能。针对此问题,将最大似然估计法应用于射手模型辨识研究中。为解决辨识过程中遇到的非线性优化问题，采用遗传算法和高斯-牛顿优化算法混合策略提高寻找全局最优解的概率，并使用单纯形法提高算法鲁棒性。基于交叉原理提出适用于导引头回路的精确模型，设计仿真实验平台并进行了多轮次人在回路实验，将输出误差框架下的最大似然估计方法成功应用于实验数据中。结果表明：混合梯度优化算法能够找到全局最优值，辨识模型能够准确反映射手本身的动态特性，辨识方法和辨识的射手模型对于光纤寻的弹药制导控制系统设计具有一定的实际意义。

关键词: 光纤图像制导武器, 射手模型, 交叉模型, 最大似然估计, 混合梯度优化, 输出误差法, 遗传算法, 高斯-牛顿优化

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

中图分类号:

TJ765.2⁺39

吴骏雄，林德福，王辉，袁亦方. 基于最大似然估计和混合梯度优化的射手模型辨识[J]. 兵工学报, 2018, 39(12): 2399-2409.

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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基于最大似然估计和混合梯度优化的射手模型辨识

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

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