量测数据自校准融合方法
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摘要
提出了一种量测数据自校准融合方法,给出量测数据系统误差(未知输入)的自识别自校准公式与计算步骤,能够自动对量测数据中事先无法校准的系统误差进行识别、估计、补偿和修正,从而减小系统误差的影响。建立多传感器的数据融合公式与计算步骤,进而降低偶然误差的影响。分别对线性系统和非线性系统进行了详细讨论,并进行了大量实例计算和仿真模拟验证。从两个算例计算结果可以看出:该方法的相对误差至少要比传统方法减小45%,且计算简单,便于工程应用。
A measurement data self-calibration fusion method was proposed.Self-recognition self-calibration formulas and computational procedures of the systematic error(unknown input)in measurement data were given,enabling to automatically identify,estimate,compensate and correct the systematic error,so as to reduce its influence.Formulas and computational procedures of multi-sensor data fusion were established to further reduce the influence of random errors.The linear system and the non-linear system were discussed in detail respectively,and a large number of examplifications and simulations were carried out.It can be seen in two examples that the relative error of the proposed method is at least 45% less than that of the traditional method,and the calculation is simple,and well-suited for engineering applications.
A measurement data self-calibration fusion method was proposed.Self-recognition self-calibration formulas and computational procedures of the systematic error(unknown input)in measurement data were given,enabling to automatically identify,estimate,compensate and correct the systematic error,so as to reduce its influence.Formulas and computational procedures of multi-sensor data fusion were established to further reduce the influence of random errors.The linear system and the non-linear system were discussed in detail respectively,and a large number of examplifications and simulations were carried out.It can be seen in two examples that the relative error of the proposed method is at least 45% less than that of the traditional method,and the calculation is simple,and well-suited for engineering applications.
引文
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[13]孙振东.面向多源数据融合的贝叶斯估计方法[J].齐鲁工业大学学报,2018,32(1):73-76.SUN Zhendong.Multi-source data fusion oriented Bayesian estimation method research[J].Journal of Qilu University of Technology,2018,32(1):73-76.(in Chinese)
[14]赵华哲,李强,杨家建.基于最小二乘原理多传感器加权数据融合[J].微型机与应用,2013,32(12):7-10.ZHAO Huazhe,LI Qiang,YANG Jiajian.A weighted multi-sensor data fusion based on least squares principle[J].Microcomputer and Its Applications,2013,32(12):7-10.(in Chinese)
[15]倪国强,李勇量,牛丽红,等.基于神经网络的数据融合技术的新进展[J].北京理工大学学报,2003,23(4):503-508.NI Guoqiang,LI Yongliang,NIU Lihong,et al.New developments in data fusion technology based on neural network[J].Transactions of Beijing Institute of Technology,2003,23(4):503-508.(in Chinese)
[16]沈飞,郭军.基于BP神经网络的数据融合方法[J].自动化与仪器仪表,2005(5):63-65.GUO Fei,SHEN Jun.Data fusion method based on BPneural network[J].Automationand Instrumentation,2005(5):63-65.(in Chinese)
[17]王楠,李文成,李岩.基于神经网络的数据融合方法[J].光机电信息,2010,27(3):36-42.WANG Nan,LI Wencheng,LI Yan.Data fusion method based on neural network[J].OME Information,2010,27(3):36-42.(in Chinese)
[18]傅惠民,吴云章,娄泰山,等.自校准Kalman滤波方法[J].航空动力学报,2014,29(6):1363-1368.FU Huimin,WU Yunzhang,LOU Taishan,et al.Self-calibration Kalman filter method[J].Journalof Aerospace Power,2014,29(6):1363-1368.(in Chinese)
[19]傅惠民,娄泰山,肖强,等.自校准扩展Kalman滤波方法[J].航空动力学报,2014,29(11):2710-2715.FU Huimin,LOU Taishan,XIAO Qiang,et al.Self-calibration extended Kalman filter method[J].Journal of Aerospace Power,2014,29(11):2710-2715.(in Chinese)
[20]傅惠民,杨海峰,肖梦丽,等.非线性状态方程自校准滤波方法[J].航空动力学报,2019,34(2):267-273.FU Huimin,YANG Haifeng,XIAO Mengli,et al.Nonlinear self-calibration Kalman filter method[J].Journal of Aerospace Power,2019,34(2):267-273.(in Chinese)
[2]王欣.多传感器数据融合问题的研究[D].长春:吉林大学,2006.WANG Xin.The research of multisensory data fusion[D].Changchun:Jilin University,2006.(in Chinese)
[3]姜延吉.多传感器数据融合关键技术研究[D].哈尔滨:哈尔滨工程大学,2010.JIANG Yanji.Research on key technology of multi-sensor data fusion[D].Harbin:Harbin Engineering University,2010.(in Chinese)
[4]KHALEGHI B,KHAMIS A,KARRAY F O,et al.Multisensor data fusion:a review of the state-of-the-art[J].Information Fusion,2013,14(1):28-44.
[5]高社生,何鹏举,杨波.组合导航原理及应用[M].西安:西北工业大学出版社,2012.
[6]胡高歌.组合导航UKF算法与数据融合技术拓展研究[D].西安:西北工业大学,2016.HU Gaoge.Extension research on UKF algorithm and data fusion technology for integrated navigation[D].Xian:Northwestern Polytechnical University,2016.(in Chinese)
[7]厉宽宽.惯导系统误差特性仿真分析及动态测试方法研究[D].山东青岛:山东科技大学,2017.LI Kuankuan.Simulation analysis and dynamic test method of inertial navigation system error characteristic[D].Qingdao Shandong:Shandong University of Science and Technology,2017.(in Chinese)
[8]徐正鹏.基于MEMS IMU的车载组合导航技术研究[D].武汉:武汉大学,2017.XU Zhengpeng.Research on vehicle integrated navigation technology based on MEMS IMU[D].Wuhan:Wuhan U-niversity,2017.(in Chinese)
[9]张晓东.可量测影像与GPS/IMU融合高精度定位定姿方法研究[D].郑州:解放军信息工程大学,2013.ZHANG Xiaodong.Research on high precision position and orientation method based on digital measurable image and GPS/IMU integration[D].Zhengzhou:Information Engineering University,2013.(in Chinese)
[10]崔波.多传感器目标跟踪数据融合关键技术研究[D].成都:西南交通大学,2012.CUI Bo.Key technologies research on multi-sensor data fusion for target tracking[D].Chengdu:Southwest Jiaotong University,2012.(in Chinese)
[11]张爱军.水下潜器组合导航定位及数据融合技术研究[D].南京:南京理工大学,2009.ZHANG Aijun.The research of integrated navigation and data fusion technology for autonomous underwater vehicle[D].Nanjing:Nanjing University of Science and Technology,2009.(in Chinese)
[12]田雪怡.多传感器数据关联与航迹融合技术研究[D].哈尔滨:哈尔滨工程大学,2012.TIAN Xueyi.The technology research of multi-sensor data association and track fusion[D].Harbin:Harbin Engineering University,2012.(in Chinese)
[13]孙振东.面向多源数据融合的贝叶斯估计方法[J].齐鲁工业大学学报,2018,32(1):73-76.SUN Zhendong.Multi-source data fusion oriented Bayesian estimation method research[J].Journal of Qilu University of Technology,2018,32(1):73-76.(in Chinese)
[14]赵华哲,李强,杨家建.基于最小二乘原理多传感器加权数据融合[J].微型机与应用,2013,32(12):7-10.ZHAO Huazhe,LI Qiang,YANG Jiajian.A weighted multi-sensor data fusion based on least squares principle[J].Microcomputer and Its Applications,2013,32(12):7-10.(in Chinese)
[15]倪国强,李勇量,牛丽红,等.基于神经网络的数据融合技术的新进展[J].北京理工大学学报,2003,23(4):503-508.NI Guoqiang,LI Yongliang,NIU Lihong,et al.New developments in data fusion technology based on neural network[J].Transactions of Beijing Institute of Technology,2003,23(4):503-508.(in Chinese)
[16]沈飞,郭军.基于BP神经网络的数据融合方法[J].自动化与仪器仪表,2005(5):63-65.GUO Fei,SHEN Jun.Data fusion method based on BPneural network[J].Automationand Instrumentation,2005(5):63-65.(in Chinese)
[17]王楠,李文成,李岩.基于神经网络的数据融合方法[J].光机电信息,2010,27(3):36-42.WANG Nan,LI Wencheng,LI Yan.Data fusion method based on neural network[J].OME Information,2010,27(3):36-42.(in Chinese)
[18]傅惠民,吴云章,娄泰山,等.自校准Kalman滤波方法[J].航空动力学报,2014,29(6):1363-1368.FU Huimin,WU Yunzhang,LOU Taishan,et al.Self-calibration Kalman filter method[J].Journalof Aerospace Power,2014,29(6):1363-1368.(in Chinese)
[19]傅惠民,娄泰山,肖强,等.自校准扩展Kalman滤波方法[J].航空动力学报,2014,29(11):2710-2715.FU Huimin,LOU Taishan,XIAO Qiang,et al.Self-calibration extended Kalman filter method[J].Journal of Aerospace Power,2014,29(11):2710-2715.(in Chinese)
[20]傅惠民,杨海峰,肖梦丽,等.非线性状态方程自校准滤波方法[J].航空动力学报,2019,34(2):267-273.FU Huimin,YANG Haifeng,XIAO Mengli,et al.Nonlinear self-calibration Kalman filter method[J].Journal of Aerospace Power,2019,34(2):267-273.(in Chinese)