基于非周期数据的惯导系统健康状态预测方法  

Health State Prediction of Inertial Navigation System Based on Aperiodic Data

作　　者：王子文 孔祥玉 周志杰 宁鹏云 张超丽 WANG Ziwen;KONG Xiangyu;ZHOU Zhijie;NING Pengyun;ZHANG Chaoli(College of Missile Engineering Rocket Force University of Engineering,Xi'an 710000 China)

机构地区：[1]火箭军工程大学导弹工程学院,西安710000

出　　处：《电光与控制》2025年第2期60-65,72,共7页Electronics Optics & Control

基　　金：国家自然科学基金(62273354,61673387,61833016)。

摘　　要：健康状态预测是保障惯导系统(INS)安全稳定运行的重要技术。针对惯导系统检测次数有限、测试时间间隔非周期性的特点,提出一种基于非周期数据的惯导系统健康状态预测方法。首先,采用梅特罗波利斯-黑斯廷斯算法结合同批次不同设备的惯导系统历史检测数据,将本设备的惯导系统检测数据周期化;其次,采用主成分分析方法对高维特征进行提取,减小数据的冗余性和相关性;最后,采用多分类支持向量机建立惯导系统的健康状态预测模型,实现对惯导系统的健康状态预测。通过某惯导系统的历史月稳标定数据验证了该方法的有效性和实用性。Health state prediction is a key technology to ensure the safe and stable operation of the Inertial Navigation System(INS).Aiming at the characteristics of an INS with limited detection times and aperiodic test interval a method for health state prediction of INS based on aperiodic data is proposed.Firstly the Metropolis-Hastings algorithm is used to combine the historical detection data of the inertial navigation system of different equipment in the same batch periodize the detection data of the inertial navigation system of this equipment.Secondly the principal component analysis method is utilized to extract high-dimensional features to reduce the redundancy and correlation of data.Finally the health state prediction model of INS is established by using multi-classification support vector machine to predict the health state of INS.The effectiveness and practicability of the proposed method is verified by the historical calibration data of an INS.

关 键 词：惯导系统 非周期数据 健康状态预测 

分 类 号：TP202[自动化与计算机技术—检测技术与自动化装置]