Microelectromechanical systems’(MEMS)inertial measurement unit(IMU)is widely used in many scenarios for its small size,low weight,and low-power consumptions.However,it possesses relatively low positioning accuracy co...Microelectromechanical systems’(MEMS)inertial measurement unit(IMU)is widely used in many scenarios for its small size,low weight,and low-power consumptions.However,it possesses relatively low positioning accuracy compared with other high-grade IMUs,as errors accumulate quickly over time.This paper mainly focuses on the error characteristics of the gyro part of MEMS IMU by analyzing different kinds of error parameters.As there is no published standard for MEMS gyro error characterization,three dominant error parameters are selected and investigated,namely,scale factor,in-run bias stability,and angular random walk.In addition,Allan variance analysis is deployed as an important part of the scheme with relative results presented in this paper.Not only is theoretical analysis presented,but experimental verification is also carried out correspondingly with an ADIS16490 MEMS IMU.By comparison,we find that the results of in-run bias stability exceed the given features by up to ten times,while the rest of the results agree quite well with the given features.Possible reasons for the exceeding part are given.Calibration testing results not only provide concrete characterization for MEMS gyro errors,but also enhance the importance of overall calibration of MEMS IMU before use.展开更多
ABSTRACT The multipath has long been considered a major error source in GPS applications .The characteristics 0f the GPS signal multipath effects are analyzed. based on which an experiment that considers the characte...ABSTRACT The multipath has long been considered a major error source in GPS applications .The characteristics 0f the GPS signal multipath effects are analyzed. based on which an experiment that considers the characteristics of dynamic deformation monitoring has been carried out. The solution results of observation data in two successive days are processed by a method,which combines the wavelet filtering and the differential correction betweentwo successive days. The research demonstrates that the multipath errors have stronger repeatability on successive days;after significantly mitigating the influence of multipath effects,the accuracy of three-dimensional positioning for GPS dynamic deformation monitoring can attain the mm level,an obvious accuracy improving particularly invertical component.The characteristics of GPS signal multipath,th eexperimental scheme and the qualitative and quantitative analysis of results are detailed.展开更多
To improve the accuracy of strapdown inertial navigation system(SINS) for long term applications,the rotation technique is employed to modulate the errors of the inertial sensors into periodically varied signals,and,a...To improve the accuracy of strapdown inertial navigation system(SINS) for long term applications,the rotation technique is employed to modulate the errors of the inertial sensors into periodically varied signals,and,as a result,to suppress the divergence of SINS errors.However,the errors of rotation platform will be introduced into SINS and might affect the final navigation accuracy.Considering the disadvantages of the conventional navigation computation scheme,an improved computation scheme of the SINS using rotation technique is proposed which can reduce the effects of the rotation platform errors.And,the error characteristics of the SINS with this navigation computation scheme are analyzed.Theoretical analysis,simulations and real test results show that the proposed navigation computation scheme outperforms the conventional navigation computation scheme,meanwhile reduces the requirement to the measurement accuracy of rotation angles.展开更多
This paper develops a feedforward neural network based input output model for a general unknown nonlinear dynamic system identification when only the inputs and outputs are accessible observations. In the developed m...This paper develops a feedforward neural network based input output model for a general unknown nonlinear dynamic system identification when only the inputs and outputs are accessible observations. In the developed model, the size of the input space is directly related to the system order. By monitoring the identification error characteristic curve, we are able to determine the system order and subsequently an appropriate network structure for systems identification. Simulation results are promising and show that generic nonlinear systems can be identified, different cases of the same system can also be discriminated by our model.展开更多
文摘Microelectromechanical systems’(MEMS)inertial measurement unit(IMU)is widely used in many scenarios for its small size,low weight,and low-power consumptions.However,it possesses relatively low positioning accuracy compared with other high-grade IMUs,as errors accumulate quickly over time.This paper mainly focuses on the error characteristics of the gyro part of MEMS IMU by analyzing different kinds of error parameters.As there is no published standard for MEMS gyro error characterization,three dominant error parameters are selected and investigated,namely,scale factor,in-run bias stability,and angular random walk.In addition,Allan variance analysis is deployed as an important part of the scheme with relative results presented in this paper.Not only is theoretical analysis presented,but experimental verification is also carried out correspondingly with an ADIS16490 MEMS IMU.By comparison,we find that the results of in-run bias stability exceed the given features by up to ten times,while the rest of the results agree quite well with the given features.Possible reasons for the exceeding part are given.Calibration testing results not only provide concrete characterization for MEMS gyro errors,but also enhance the importance of overall calibration of MEMS IMU before use.
文摘ABSTRACT The multipath has long been considered a major error source in GPS applications .The characteristics 0f the GPS signal multipath effects are analyzed. based on which an experiment that considers the characteristics of dynamic deformation monitoring has been carried out. The solution results of observation data in two successive days are processed by a method,which combines the wavelet filtering and the differential correction betweentwo successive days. The research demonstrates that the multipath errors have stronger repeatability on successive days;after significantly mitigating the influence of multipath effects,the accuracy of three-dimensional positioning for GPS dynamic deformation monitoring can attain the mm level,an obvious accuracy improving particularly invertical component.The characteristics of GPS signal multipath,th eexperimental scheme and the qualitative and quantitative analysis of results are detailed.
基金Project(60604011) supported by the National Natural Science Foundation of China
文摘To improve the accuracy of strapdown inertial navigation system(SINS) for long term applications,the rotation technique is employed to modulate the errors of the inertial sensors into periodically varied signals,and,as a result,to suppress the divergence of SINS errors.However,the errors of rotation platform will be introduced into SINS and might affect the final navigation accuracy.Considering the disadvantages of the conventional navigation computation scheme,an improved computation scheme of the SINS using rotation technique is proposed which can reduce the effects of the rotation platform errors.And,the error characteristics of the SINS with this navigation computation scheme are analyzed.Theoretical analysis,simulations and real test results show that the proposed navigation computation scheme outperforms the conventional navigation computation scheme,meanwhile reduces the requirement to the measurement accuracy of rotation angles.
文摘This paper develops a feedforward neural network based input output model for a general unknown nonlinear dynamic system identification when only the inputs and outputs are accessible observations. In the developed model, the size of the input space is directly related to the system order. By monitoring the identification error characteristic curve, we are able to determine the system order and subsequently an appropriate network structure for systems identification. Simulation results are promising and show that generic nonlinear systems can be identified, different cases of the same system can also be discriminated by our model.
