基于分层置信规则库的惯导系统性能评估方法

doi:10.7527/S1000-6893.2020.24456

电子电气工程与控制

本期目录 | 过刊浏览 | 高级检索

前一篇 | 后一篇

基于分层置信规则库的惯导系统性能评估方法

董昕昊, 周志杰, 胡昌华, 冯志超, 曹友

火箭军工程大学导弹工程学院, 西安 710025

收稿日期:2020-06-24 修回日期:2020-07-30 发布日期:2020-09-04
通讯作者: 董昕昊 E-mail:15991896070@163.com
基金资助:
国家自然科学基金（61370031，61773388，61374138，71601168）

Performance evaluation method for inertial system based on hierarchical belief rule base

DONG Xinhao, ZHOU Zhijie, HU Changhua, FENG Zhichao, CAO You

Missile Engineering College, Rocket Force University of Engineering, Xi'an 710025 China

Received:2020-06-24 Revised:2020-07-30 Published:2020-09-04
Supported by:
National Natural Science Foundation of China (61370031,61773388,61374138,71601168)

摘要/Abstract

摘要： 为解决惯导系统（INS）性能评估所面临的高价值样本缺失、评估指标多、系统复杂等问题，提出一种基于分层置信规则库（Hierarchical BRB）的惯导系统性能评估方法。将专家知识与监测数据进行有效融合，提高了惯导系统的性能评估精度。首先，针对惯导系统结构构建分层BRB模型，同时将系统内部器件产生组合误差考虑在模型中。其次，为降低专家知识不确定性对初始模型评估精度的影响，采用基于投影算子的协方差矩阵自适应优化策略（P-CMA-ES）构建优化模型，通过监测数据对模型参数进行微调。最后，以某型捷联惯导系统的性能评估为例，验证了所提方法的有效性。

关键词: 惯导系统, 性能评估, 置信规则库, 专家系统, 小样本

Abstract: To solve the problems of high-value sample shortage, multiple evaluation indicators, and system complexity in the performance evaluation of the Inertial Navigation System (INS), we propose a performance evaluation method for the INS based on the hierarchical Belief Rule Base (BRB). By integrating the expert knowledge and the monitoring data, the performance evaluation accuracy of the INS is significantly improved. Firstly, a hierarchical BRB model is established for the INS structure, considering the combined errors generated by the internal components of the system. Then, to reduce the influence of expert knowledge uncertainty on the evaluation accuracy of the initial model, the Projection operator-based Covariance Matrix Adaptive optimization Strategy (P-CMA-ES) is employed to construct the optimization model, where the model parameters are fine-tuned using the monitoring data. Finally, a certain type of strapdown INS is taken as an example, verifying the effectiveness of the proposed method.

Key words: Inertial Navigation System (INS), performance evaluation, Belief Rule Base (BRB), expert system, small sample

中图分类号:

V249.32⁺2

董昕昊, 周志杰, 胡昌华, 冯志超, 曹友. 基于分层置信规则库的惯导系统性能评估方法[J]. 航空学报, 2021, 42(7): 324456-324456.

DONG Xinhao, ZHOU Zhijie, HU Changhua, FENG Zhichao, CAO You. Performance evaluation method for inertial system based on hierarchical belief rule base[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(7): 324456-324456.

参考文献

[1] 牛小骥, 班亚龙, 张提升, 等. GNSS/INS深组合技术研究进展与展望[J]. 航空学报, 2016, 37(10):2895-2908. NIU X J, BAN Y L, ZHANG T S, et al. Research progress and prospects of GNSS/INS deep integration[J]. Acta Aeronautica et Astronautica Sinica, 2016, 37(10):2895-2908(in Chinese).
[2] 钱超, 张子剑, 李大伟. 平台式惯性导航系统在线可靠性评估技术[J]. 航空学报, 2017, 38(9):321259. QIAN C, ZHANG Z J, LI D W. On-line reliability assessment of platform inertial navigation system[J]. Acta Aeronautica et Astronautica Sinica, 2017, 38(9):321259(in Chinese).
[3] 戴庆天. 惯导组件故障诊断技术研究[D]. 天津:中国民航大学, 2015. DAI Q T. The research of INU fault diagnosis technique[D]. Tianjin:Civil Aviation University of China, 2015(in Chinese).
[4] 鲁峰, 黄金泉. 航空发动机部件性能参数融合预测[J]. 航空学报, 2009, 30(10):1795-1800. LU F, HUANG J Q. Engine component performance prognostics based on decision fusion[J]. Acta Aeronautica et Astronautica Sinica, 2009, 30(10):1795-1800(in Chinese).
[5] 陈雷雨,周志杰,唐帅文,等.融合多元信息的武器装备性能评估方法[J].系统工程与电子技术, 2021, 42(7):1527-1533. CHEN L Y, ZHOU Z J, TANG S W, et al. Performance evaluation of weaponry equipment by fusing multiple information[J]. Journal of Systems Engineering and Electronics, 2021, 42(7):1527-1533(in Chinese).
[6] 赵新超, 吕卫民, 金国富. 基于性能退化与D-S证据理论的可靠性评估方法[J]. 兵工自动化, 2019, 38(4):53-56, 83. ZHAO X C, LYU W M, JIN G F. Reliability evaluation method based on performance degradation and D-S evidence theory[J]. Ordnance Industry Automation, 2019, 38(4):53-56, 83(in Chinese).
[7] 王欣宇, 朱嘉林, 缪旻. 基于阈值降噪与神经网络的陀螺仪误差补偿方法[J]. 北京信息科技大学学报(自然科学版), 2020, 35(1):58-62, 77. WANG X Y, ZHU J L, MIAO M. Gyroscope error compensation method based on threshold denoising and neural network[J]. Journal of Beijing Information Science & Technology University, 2020, 35(1):58-62, 77(in Chinese).
[8] 于恩宁. 激光捷联惯性测量组合故障诊断技术研究[D]. 哈尔滨:哈尔滨工业大学, 2018. YU E N. The research of fault diagnosis technology on laser gyro strapdown inertial measurement unit[D]. Harbin:Harbin Institute of Technology, 2018(in Chinese).
[9] 李永满. 某型激光陀螺捷联惯组测试系统设计[D]. 哈尔滨:哈尔滨工业大学, 2013. LI Y M. The test system design on the measurement unit of laser gyro strapdown inertial[D]. Harbin:Harbin Institute of Technology, 2013(in Chinese).
[10] YANG J B, LIU J, WANG J, et al. Belief rule-base inference methodology using the evidential reasoning Approach-RIMER[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part A:Systems and Humans, 2006, 36(2):266-285.
[11] YANG J B, LIU J, XU D L, et al. Optimization models for training belief-rule-based systems[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part A:Systems and Humans, 2007, 37(4):569-585.
[12] XU D L, LIU J, YANG J B, et al. Inference and learning methodology of belief-rule-based expert system for pipeline leak detection[J]. Expert Systems with Applications, 2007, 32(1):103-113.
[13] 胡昌华, 司小胜. 基于信度规则库的惯性平台健康状态参数在线估计[J]. 航空学报, 2010, 31(7):1454-1465. HU C H, SI X S. Real-time parameters estimation of inertial platform's health condition based on belief rule base[J]. Acta Aeronautica et Astronautica Sinica, 2010, 31(7):1454-1465(in Chinese).
[14] ZHOU Z J, HU G Y, ZHANG B C, et al. A model for hidden behavior prediction of complex systems based on belief rule base and power set[J]. IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2018, 48(9):1649-1655.
[15] YIN X J, WANG Z L, ZHANG B C, et al. A double layer BRB model for health prognostics in complex electromechanical system[J]. IEEE Access, 2017, 5:23833-23847.
[16] YANG J B, WANG Y M, XU D L, et al. Belief rule-based methodology for mapping consumer preferences and setting product targets[J]. Expert Systems with Applications, 2012, 39(5):4749-4759.
[17] HE W, HU G Y, ZHOU Z J, et al. A new hierarchical belief-rule-based method for reliability evaluation of wireless sensor network[J]. Microelectronics Reliability, 2018, 87:33-51.
[18] CHEN Y W, YANG J B, XU D L, et al. Inference analysis and adaptive training for belief rule based systems[J]. Expert Systems With Applications, 2011, 38(10):12845-12860.
[19] YANG J B, XU D L. Evidential reasoning rule for evidence combination[J]. Artificial Intelligence, 2013, 205:1-29.
[20] HU G Y, ZHOU Z J, ZHANG B C, et al. A method for predicting the network security situation based on hidden BRB model and revised CMA-ES algorithm[J]. Applied Soft Computing, 2016, 48:404-418.
[21] 蔚国强, 杨建业, 张合新. 激光陀螺捷联惯组的无定向快速标定技术研究[J]. 中国惯性技术学报, 2011, 19(1):21-27. WEI G Q, YANG J Y, ZHANG H X. Rapid calibration of laser gyro strapdown IMUs without north-seeking[J]. Journal of Chinese Inertial Technology, 2011, 19(1):21-27(in Chinese).

E-mail：hkxb@buaa.edu.cn

关于我们

期刊社服务

专业学科

封面文章

友情链接

主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

基于分层置信规则库的惯导系统性能评估方法

Performance evaluation method for inertial system based on hierarchical belief rule base

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	何华锋, 王依繁, 何耀民, 苏敬, 韩晓斐. 导弹武器系统时间同步网的综合性能评估方法[J]. 航空学报, 2021, 42(6): 324564-324564.
[2]	刘付成, 牟金震, 刘宗明, 韩飞, 李爽. 低照度小样本限制下的失效卫星相对位姿估计与优化[J]. 航空学报, 2021, 42(4): 524984-524984.
[3]	万鹏, 李迎光, 刘长青, 华家玘. 基于域对抗门控网络的变工况刀具磨损精确预测方法[J]. 航空学报, 2021, 42(10): 524879-524879.
[4]	梁帅, 杨林, 杨朝旭, 许斌. 基于Kalman滤波的变体飞行器T-S模糊控制[J]. 航空学报, 2020, 41(S2): 724274-724274.
[5]	宋闯, 赵佳佳, 王康, 梁欣凯. 面向智能感知的小样本学习研究综述[J]. 航空学报, 2020, 41(S1): 723756-723756.
[6]	黄卫权, 方涛, 王宗义. 改进的格网惯导系统无阻尼综合校正方法[J]. 航空学报, 2020, 41(9): 323921-323921.
[7]	卢航, 郝顺义, 彭志颖, 黄国荣. 基于边缘采样的简化高阶CKF在非线性快速传递对准中的应用[J]. 航空学报, 2019, 40(3): 322390-322390.
[8]	徐颖强, 陈仙亮, 曹栋波. 样本量为2的极小样本相容性检验方法[J]. 航空学报, 2018, 39(5): 221936-221936.
[9]	郭琦, 周召发, 孙立江, 徐梓皓, 陈河. 单轴旋转捷联惯导系统尺寸参数辨识与转位设计[J]. 航空学报, 2017, 38(10): 320841-320841.
[10]	郝玉锴, 崔西宁, 李雷雷, 杨琼. 机载嵌入式系统基准测试方法[J]. 航空学报, 2016, 37(4): 1327-1335.
[11]	任磊, 杜建邦, 王美娥. 旋转惯导中加速度计尺寸效应误差分析及补偿[J]. 航空学报, 2013, 34(6): 1424-1435.
[12]	王明珠, 邓寅东, 常秀娟. 基于偏最小二乘回归法的飞机总工时预测模型研究[J]. 航空学报, 2012, 33(8): 1448-1454.
[13]	谢阳光, 伊国兴, 王常虹, 曲耀斌. 高斯-厄米特滤波器在捷联惯导系统初始对准中的应用[J]. 航空学报, 2012, (3): 554-560.
[14]	王跃钢, 杨家胜, 杨波. 纬度未知条件下捷联惯导系统晃动基座的初始对准[J]. 航空学报, 2012, 33(12): 2322-2329.
[15]	屠毅;林贵平. 大型飞机座舱温度控制系统仿真[J]. 航空学报, 2011, 32(1): 49-57.