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初始标定    
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  initial calibration
    Federated Kalman Fitering and its Application in the Initial Calibration of Integrated INS
    联合卡尔曼滤波及其在综合惯性导航系统初始标定中的应用
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    This paper proposes an adaptive robust filtering technique and applies it in the initial calibration of ESGM.
    本文提出一种自适应鲁棒滤波方法,并应用于ESGM的初始标定
短句来源
  initial calibration
    Federated Kalman Fitering and its Application in the Initial Calibration of Integrated INS
    联合卡尔曼滤波及其在综合惯性导航系统初始标定中的应用
短句来源
    This paper proposes an adaptive robust filtering technique and applies it in the initial calibration of ESGM.
    本文提出一种自适应鲁棒滤波方法,并应用于ESGM的初始标定
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  initial calibration
After an initial calibration with buffers, they can measure pH over extended periods (more than 40?h).
      
These results suggest that the initial calibration of grip force uses veridical information about the weight of the object provided by the other hand.
      
A single initial calibration of the system outside of the test section is all that is necessary and no subsequent manual re-positioning is required during experimentation.
      
We conclude that, after an initial calibration step, the slope method allows accurate measurement of interstitial muscle metabolites and it could be used to monitor rapid metabolic changes during exercise.
      
The laboratory values differ significantly from the "field" values of K'; these results suggest that the effectiveness of GPR at any site can be substantially improved by initial calibration of well-exposed locations.
      
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This paper studies emphatically on the problem of filtering divergence when applies kalman filtering in the parameters estimation of electrostatic supported gyro monitor(ESGM).Due to uncertainty in both system itself and the circumstance condition,it's difficult to take an accurate mathematics description of every state of the system.This may cause the filter unstability even divergence.This paper proposes an adaptive robust filtering technique and applies it in the initial calibration of ESGM.The result indicates...

This paper studies emphatically on the problem of filtering divergence when applies kalman filtering in the parameters estimation of electrostatic supported gyro monitor(ESGM).Due to uncertainty in both system itself and the circumstance condition,it's difficult to take an accurate mathematics description of every state of the system.This may cause the filter unstability even divergence.This paper proposes an adaptive robust filtering technique and applies it in the initial calibration of ESGM.The result indicates that this algorithm may efficiently restrain the filter diverging caused by incorrect mathematics model and greatly improve the accuracy of parameter estimation.

本文重点研究了卡尔曼滤波在静电陀螺监控器(ESGM)参数估计应用中滤波发散的问题。由于系统本身和外部条件的不确定性,很难对系统各状态进行准确的数学描述,造成滤波器不稳定甚至发散。本文提出一种自适应鲁棒滤波方法,并应用于ESGM的初始标定。研究结果表明,应用该算法,可以有效地抑制由于模型不准而产生的滤波发散现象,大大提高了参数估计的精度。

At present,the correcting method of sensor error depends on sensor prime data so that when working time increases and sensor parameter is changed,the correcting error will increase.According to this instance,the method of auto-calibration sensor is studied in which using multi-benchmark cource current simulates normal pressure so that reat-time output curve can be obtained and measurement pressure can be calculated by reat-time output curve.Experiment indicates that above method has practicality and 0.15?% accuracy...

At present,the correcting method of sensor error depends on sensor prime data so that when working time increases and sensor parameter is changed,the correcting error will increase.According to this instance,the method of auto-calibration sensor is studied in which using multi-benchmark cource current simulates normal pressure so that reat-time output curve can be obtained and measurement pressure can be calculated by reat-time output curve.Experiment indicates that above method has practicality and 0.15?% accuracy can be achieved in a certain temperature and pressure range if four benchmark sources can be used.

目前的传感器误差校正方法,由于都是以传感器初始标定数据作为依据,从而随着使用时间的增加,传感器参数发生变化,其校正误差会逐步增大。针对这种情况,研究了用多基准恒流源模拟标准压力自动对传感器进行标定得到实时输出特性曲线,并据此求得测量压力。实验结果表明:该校正方法切实可行,若采用4个基准源校正,可在一定的温度和压力范围内实现0.15%的精度。

The structure and real-time correction of sample set of pressure sensor's intput/output characteristic are studied.First,the time-drift of sensor's characteristic and its change law with temperature are obtained by the accelerated test,then the sample set is established.The dynamic contrast experiment for the fusion accuracy of sample set based on BP neural network model is made and the rationality of sample set structure is demonstrated.Furthermore,a kind of method of correcting sample set real-time is presented.The...

The structure and real-time correction of sample set of pressure sensor's intput/output characteristic are studied.First,the time-drift of sensor's characteristic and its change law with temperature are obtained by the accelerated test,then the sample set is established.The dynamic contrast experiment for the fusion accuracy of sample set based on BP neural network model is made and the rationality of sample set structure is demonstrated.Furthermore,a kind of method of correcting sample set real-time is presented.The correction process is controlled by programme.Making comparison between real-time correction data and calibration data,and another calibration data which is obained after the accele-rated test in 600 h.The maximum error is only 0.08 kPa.After sample set is corrected,its accuracy is improved almost an order of magnitude.

研究了压力传感器输入/输出特性样本集结构和样本集的实时校正。首先,通过加速试验获得了传感器特性的时漂以及受温度影响的变化规律,并据此构建了样本集。基于BP神经网络模型,对样本集的融合精度进行了动态对比试验,进而验证了样本集构建的合理性。此外,提出一种对样本集进行实时校正的方法,校正过程由程序控制。将实时校正后的数据与初始标定样本数据、加速试验600 h后的标定数据对比,最大偏差仅为0.08 kPa,样本集经校正后,数据准确度提高了近1个数量级。

 
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