摘要
通过阵列惯性器件构成的可穿戴自主定位系统,可显著提高人员的定位精度,但是可穿戴自主定位系统中的阵列式惯性器件在工作过程中难以避免出现故障。针对应急救援人员穿戴的自主定位系统中阵列加速度计的噪声增大现象,提出了一种基于卷积神经网络的阵列加速度计故障检测方法,使用广义似然比检验对比得到阵列陀螺仪对照数据,再通过CNN计算加速度计数据与陀螺仪对照数据的映射结果,实现了对阵列加速度计噪声增大故障的快速检测。通过十二IMU阵列数据融合和故障检测试验结果表明,该检测方法能够快速有效检测地出阵列惯性器件中的加速度计噪声增大典型故障,故障检测率≥98%,效果明显。
The wearable autonomous positioning system composed by array inertial devices can significantly improve the positioning accuracy of the wearer,but the array inertial devices in the wearable autonomous positioning system are difficult to avoid failure in the process of operation.To address the phenomenon of array accelerometer noise increasing fault in the autonomous positioning system worn by emergency rescue personnel,a Convolutional Neural Networks(CNN)based array accelerometer fault detection method is proposed,using the Generalized Likelihood Ratio(GLR)test to compare the array gyroscope with the array accelerometer,The GLR test is used to compare the array gyroscope control data,and then the CNN calculates the mapping result between the accelerometer data and the gyroscope control data to achieve fast detection of the array accelerometer growth increase fault.Through the twelve IMU array data fusion and fault detection test results show that the detection method can quickly and effectively detect the typical fault of accelerometer noise increase in the array inertial device,the fault detection rate≥98%,the effect is obvious.
作者
陈豪
苏中
徐湛
Chen Hao;Su Zhong;Xu Zhan(Beijing Key Laboratory of High Dynamic Navigation Technology,Beijing Information Science&Technology University,Beijing 100101,China;Key Laboratory of Modern Measurement and Control Technology,Ministry of Education,Beijing Information Science&Technology University,Beijing 100192,China)
出处
《电子测量技术》
北大核心
2023年第16期105-111,共7页
Electronic Measurement Technology
基金
国家重点研发计划课题(2020YFC1511702)
国家自然科学基金(61771059,61801032)
北京市自然科学基金(4212003)项目资助
关键词
阵列惯性器件
故障检测
CNN
广义似然比
可穿戴装备
array inertial navigation
fault diagnosis
CNN
generalized likelihood ratio
wearable gear
作者简介
陈豪,硕士研究生,主要研究方向为行人定位、故障诊断;苏中,博士,教授,主要研究方向为盲环境智能导航、高动态导航与控制。E-mail:sz@bistu.edu.cn
