The inertial navigation system(INS),which is frequently used in emergency rescue operations and other situations,has the benefits of not relying on infrastructure,high positioning frequency,and strong real-time perfor...The inertial navigation system(INS),which is frequently used in emergency rescue operations and other situations,has the benefits of not relying on infrastructure,high positioning frequency,and strong real-time performance.However,the intricate and unpredictable pedestrian motion patterns lead the INS localization error to significantly diverge with time.This paper aims to enhance the accuracy of zero-velocity interval(ZVI)detection and reduce the heading and altitude drift of foot-mounted INS via deep learning and equation constraint of dual feet.Aiming at the observational noise problem of low-cost inertial sensors,we utilize a denoising autoencoder to automatically eliminate the inherent noise.Aiming at the problem that inaccurate detection of the ZVI detection results in obvious displacement error,we propose a sample-level ZVI detection algorithm based on the U-Net neural network,which effectively solves the problem of mislabeling caused by sliding windows.Aiming at the problem that Zero-Velocity Update(ZUPT)cannot suppress heading and altitude error,we propose a bipedal INS method based on the equation constraint and ellipsoid constraint,which uses foot-to-foot distance as a new observation to correct heading and altitude error.We conduct extensive and well-designed experiments to evaluate the performance of the proposed method.The experimental results indicate that the position error of our proposed method did not exceed 0.83% of the total traveled distance.展开更多
To improve the reliability and accuracy of the global po- sitioning system （GPS）/micro electromechanical system （MEMS）- inertial navigation system （INS） integrated navigation system, this paper proposes two diff...To improve the reliability and accuracy of the global po- sitioning system （GPS）/micro electromechanical system （MEMS）- inertial navigation system （INS） integrated navigation system, this paper proposes two different methods. Based on wavelet threshold denoising and functional coefficient autoregressive （FAR） model- ing, a combined data processing method is presented for MEMS inertial sensor, and GPS attitude information is also introduced to improve the estimation accuracy of MEMS inertial sensor errors. Then the positioning accuracy during GPS signal short outage is enhanced. To improve the positioning accuracy when a GPS signal is blocked for long time and solve the problem of the tra- ditional adaptive neuro-fuzzy inference system （ANFIS） method with poor dynamic adaptation and large calculation amount, a self-constructive ANFIS （SCANFIS） combined with the extended Kalman filter （EKF） is proposed for MEMS-INS errors modeling and predicting. Experimental road test results validate the effi- ciency of the proposed methods.展开更多
To improve the precision of inertial navigation system（INS） during long time operation,the rotation modulated technique（RMT） was employed to modulate the errorr of the inertial sensors into periodically varied sig...To improve the precision of inertial navigation system（INS） during long time operation,the rotation modulated technique（RMT） was employed to modulate the errorr of the inertial sensors into periodically varied signals,and,as a result,to suppress the divergence of INS errors.The principle of the RMT was introduced and the error propagating functions were derived from the rotary navigation equation.Effects of the measurement error for the rotation angle of the platform on the system precision were analyzed.The simulation and experimental results show that the precision of INS was ① dramatically improved with the use of the RMT,and ② hardly reduced when the measurement error for the rotation angle was in arc-second level.The study results offer a theoretical basis for engineering design of rotary INS.展开更多
The dual-axis rotational inertial navigation system(INS)with dithered ring laser gyro(DRLG)is widely used in high precision navigation.The major inertial sensor errors such as drift errors of gyro and accelerometer ca...The dual-axis rotational inertial navigation system(INS)with dithered ring laser gyro(DRLG)is widely used in high precision navigation.The major inertial sensor errors such as drift errors of gyro and accelerometer can be averaged out,but the G-sensitive drifts of laser gyro cannot be averaged out by indexing.A 16-position rotational simulation experiment proves the G-sensitive drift will affect the long-term navigation error for the rotational INS quantitatively.The vibration coupling and asymmetric structure of the DRLG are the main errors.A new dithered mechanism and optimized DRLG is designed.The validity and efficiency of the optimized design are conformed by 1 g sinusoidal vibration experiments.An optimized inertial measurement unit(IMU)is formulated and measured experimentally.Laboratory and vehicle experimental results show that the divergence speed of longitude errors can be effectively slowed down in the optimized IMU.In long term independent navigation,the position accuracy of dual-axis rotational INS is improved close to 50%,and the G-sensitive drifts of laser gyro in the optimized IMU are less than 0.0002°/h.These results have important theoretical significance and practical value for improving the structural dynamic characteristics of DRLG INS,especially the highprecision inertial system.展开更多
The integration of GNSS （Global Navigation Satellite System） and INS （Inertial Navigation System） using IMU （Inertial Measurement Unit） is now widely used for MMS （Mobile Mapping System） and navigation applica...The integration of GNSS （Global Navigation Satellite System） and INS （Inertial Navigation System） using IMU （Inertial Measurement Unit） is now widely used for MMS （Mobile Mapping System） and navigation applications to seamlessly determine position, velocity and attitude of the mobile platform. With low cost, small size, ligh weight and low power consumtion, the MEMS （Micro-Electro-Mechanical System） IMU and low cost GPS （Global Positioning System） receivers are now the trend in research and using for many applications. However, researchs in the literature indicated that the the performance of the low cost INS/GPS systems is still poor, particularly, in case of GNSS-noise environment. To overcome this problem, this research applies analytic contrains including non-holonomic constraint and zero velocity update in the data fusion engine such as Extended Kalman Filter to improve the performance of the system. The benefit of the proposed method will be demonstrated through experiments and data analysis.展开更多
Aiming at the problem of poor observability of measurement information in the loosely-coupled integration of the inertial navigation system （INS） and the wireless sensor network （WSN）, this paper presents a tightl...Aiming at the problem of poor observability of measurement information in the loosely-coupled integration of the inertial navigation system （INS） and the wireless sensor network （WSN）, this paper presents a tightly-coupled integration based on the Kalman filter （KF）. When the WSN is available, the difference between the distances from the blind node（BN） to the reference nodes （RNs） measured by the INS and those measured by the WSN are used as measurement information for the KF due to its better observability and independence, which can effectively improve the accuracy of the KF. Simulations show that the proposed approach reduces the mean error of the position by about 50% compared with loosely-coupled integration, while the mean error of the velocity is a little higher than that of loosely-coupled integration.展开更多
In view of the failure of GNSS signals,this paper proposes an INS/GNSS integrated navigation method based on the recurrent neural network(RNN).This proposed method utilizes the calculation principle of INS and the mem...In view of the failure of GNSS signals,this paper proposes an INS/GNSS integrated navigation method based on the recurrent neural network(RNN).This proposed method utilizes the calculation principle of INS and the memory function of the RNN to estimate the errors of the INS,thereby obtaining a continuous,reliable and high-precision navigation solution.The performance of the proposed method is firstly demonstrated using an INS/GNSS simulation environment.Subsequently,an experimental test on boat is also conducted to validate the performance of the method.The results show a promising application prospect for RNN in the field of positioning for INS/GNSS integrated navigation in the absence of GNSS signal,as it outperforms extreme learning machine(ELM)and EKF by approximately 30%and 60%,respectively.展开更多
The interest for land navigation has increased for the recent years. With the advent of the Global Position System (GPS) we have now the ability to determine the absolute position anywhere on the globe. The problem is...The interest for land navigation has increased for the recent years. With the advent of the Global Position System (GPS) we have now the ability to determine the absolute position anywhere on the globe. The problem is that the GPS systems work well only in open environments with no overhead obstructions and they are subject to large unavoidable errors when the reception from some of the satellites are blocked. This occurs frequently in urban environments, forests and tunnels. GPS systems require at least four “visible” satellites to maintain a good position fix. In many situations in which higher level of accuracy is required, the navigation cannot be achieved by GPS alone. This paper discusses the design of a reliable multisensor fusion algorithm using GPS and Inertial Navigation System in order to decrease the implementation cost of such systems on land vehicles. The major contribution of this paper is in the definition of the possible developments and research axes in land navigation.展开更多
This paper proposes a technique that global positioning system(GPS)combines inertial navigation system(INS)by using unscented particle filter(UPF)to estimate the exact outdoor position.This system can make up for the ...This paper proposes a technique that global positioning system(GPS)combines inertial navigation system(INS)by using unscented particle filter(UPF)to estimate the exact outdoor position.This system can make up for the weak point on position estimation by the merits of GPS and INS.In general,extended Kalman filter(EKF)has been widely used in order to combine GPS with INS.However,UPF can get the position more accurately and correctly than EKF when it is applied to real-system included non-linear,irregular distribution errors.In this paper,the accuracy of UPF is proved through the simulation experiment,using the virtual-data needed for the test.展开更多
为了降低行人航位推算(Pedestrian dead reckoning,PDR)算法在进行井下人员定位时产生的累积误差,提出了一种基于PDR算法与伪平面技术的井下人员定位方法。首先,采用惯性导航传感器获取井下人员的步态信息,通过线性步长估计模型和四元...为了降低行人航位推算(Pedestrian dead reckoning,PDR)算法在进行井下人员定位时产生的累积误差,提出了一种基于PDR算法与伪平面技术的井下人员定位方法。首先,采用惯性导航传感器获取井下人员的步态信息,通过线性步长估计模型和四元数法实现步长估计和方向估计,利用PDR算法推算人员的位置;其次,使用井下人员活动区域以及预设的标记点构建伪平面,并将井下人员位置映射到伪平面坐标上,为降低PDR算法的累积误差做准备;最后,采用SVM进行井下人员活动检测,通过转弯活动判断其是否处于特殊标记点,将PDR解算的位置与伪平面内已知转弯位置标记点进行相关性分析,完成伪平面信息与工人位置的匹配,校准并更新PDR位置,降低累积误差。结果表明:井下工人在完成单个转弯活动过程中,传统PDR算法解算位置平均误差为0.98 m,而进行伪平面修正后平均误差降低到0.31 m;在完成区域性多活动过程中,采用伪平面技术修正后的PDR平均定位误差从1.08 m降低到0.38 m。因此,所提出的井下人员定位方法有效提高了PDR算法的定位精度。展开更多
基金supported in part by National Key Research and Development Program under Grant No.2020YFB1708800China Postdoctoral Science Foundation under Grant No.2021M700385+5 种基金Guang Dong Basic and Applied Basic Research Foundation under Grant No.2021A1515110577Guangdong Key Research and Development Program under Grant No.2020B0101130007Central Guidance on Local Science and Technology Development Fund of Shanxi Province under Grant No.YDZJSX2022B019Fundamental Research Funds for Central Universities under Grant No.FRF-MP-20-37Interdisciplinary Research Project for Young Teachers of USTB(Fundamental Research Funds for the Central Universities)under Grant No.FRF-IDRY-21-005National Natural Science Foundation of China under Grant No.62002026。
文摘The inertial navigation system(INS),which is frequently used in emergency rescue operations and other situations,has the benefits of not relying on infrastructure,high positioning frequency,and strong real-time performance.However,the intricate and unpredictable pedestrian motion patterns lead the INS localization error to significantly diverge with time.This paper aims to enhance the accuracy of zero-velocity interval(ZVI)detection and reduce the heading and altitude drift of foot-mounted INS via deep learning and equation constraint of dual feet.Aiming at the observational noise problem of low-cost inertial sensors,we utilize a denoising autoencoder to automatically eliminate the inherent noise.Aiming at the problem that inaccurate detection of the ZVI detection results in obvious displacement error,we propose a sample-level ZVI detection algorithm based on the U-Net neural network,which effectively solves the problem of mislabeling caused by sliding windows.Aiming at the problem that Zero-Velocity Update(ZUPT)cannot suppress heading and altitude error,we propose a bipedal INS method based on the equation constraint and ellipsoid constraint,which uses foot-to-foot distance as a new observation to correct heading and altitude error.We conduct extensive and well-designed experiments to evaluate the performance of the proposed method.The experimental results indicate that the position error of our proposed method did not exceed 0.83% of the total traveled distance.
基金supported by the National Natural Science Foundation of China (60902055)
文摘To improve the reliability and accuracy of the global po- sitioning system （GPS）/micro electromechanical system （MEMS）- inertial navigation system （INS） integrated navigation system, this paper proposes two different methods. Based on wavelet threshold denoising and functional coefficient autoregressive （FAR） model- ing, a combined data processing method is presented for MEMS inertial sensor, and GPS attitude information is also introduced to improve the estimation accuracy of MEMS inertial sensor errors. Then the positioning accuracy during GPS signal short outage is enhanced. To improve the positioning accuracy when a GPS signal is blocked for long time and solve the problem of the tra- ditional adaptive neuro-fuzzy inference system （ANFIS） method with poor dynamic adaptation and large calculation amount, a self-constructive ANFIS （SCANFIS） combined with the extended Kalman filter （EKF） is proposed for MEMS-INS errors modeling and predicting. Experimental road test results validate the effi- ciency of the proposed methods.
基金Sponsored by the National Natural Science Foundation of China(60604011)
文摘To improve the precision of inertial navigation system（INS） during long time operation,the rotation modulated technique（RMT） was employed to modulate the errorr of the inertial sensors into periodically varied signals,and,as a result,to suppress the divergence of INS errors.The principle of the RMT was introduced and the error propagating functions were derived from the rotary navigation equation.Effects of the measurement error for the rotation angle of the platform on the system precision were analyzed.The simulation and experimental results show that the precision of INS was ① dramatically improved with the use of the RMT,and ② hardly reduced when the measurement error for the rotation angle was in arc-second level.The study results offer a theoretical basis for engineering design of rotary INS.
基金supported by the National Natural Science Foundation of China(61503399).
文摘The dual-axis rotational inertial navigation system(INS)with dithered ring laser gyro(DRLG)is widely used in high precision navigation.The major inertial sensor errors such as drift errors of gyro and accelerometer can be averaged out,but the G-sensitive drifts of laser gyro cannot be averaged out by indexing.A 16-position rotational simulation experiment proves the G-sensitive drift will affect the long-term navigation error for the rotational INS quantitatively.The vibration coupling and asymmetric structure of the DRLG are the main errors.A new dithered mechanism and optimized DRLG is designed.The validity and efficiency of the optimized design are conformed by 1 g sinusoidal vibration experiments.An optimized inertial measurement unit(IMU)is formulated and measured experimentally.Laboratory and vehicle experimental results show that the divergence speed of longitude errors can be effectively slowed down in the optimized IMU.In long term independent navigation,the position accuracy of dual-axis rotational INS is improved close to 50%,and the G-sensitive drifts of laser gyro in the optimized IMU are less than 0.0002°/h.These results have important theoretical significance and practical value for improving the structural dynamic characteristics of DRLG INS,especially the highprecision inertial system.
文摘The integration of GNSS （Global Navigation Satellite System） and INS （Inertial Navigation System） using IMU （Inertial Measurement Unit） is now widely used for MMS （Mobile Mapping System） and navigation applications to seamlessly determine position, velocity and attitude of the mobile platform. With low cost, small size, ligh weight and low power consumtion, the MEMS （Micro-Electro-Mechanical System） IMU and low cost GPS （Global Positioning System） receivers are now the trend in research and using for many applications. However, researchs in the literature indicated that the the performance of the low cost INS/GPS systems is still poor, particularly, in case of GNSS-noise environment. To overcome this problem, this research applies analytic contrains including non-holonomic constraint and zero velocity update in the data fusion engine such as Extended Kalman Filter to improve the performance of the system. The benefit of the proposed method will be demonstrated through experiments and data analysis.
基金The National Basic Research Program of China(973 Program)(No.2009CB724002)the National Natural Science Foundation of China(No.50975049)+3 种基金the Specialized Research Fund for the Doctoral Program of Higher Education of China(No.20110092110039)the Aviation Science Foundation(No.20090869008)the Six Peak Talents Foundation in Jiangsu Province(No.2008143)Program of Scientific Innovation Research of College Graduate in Jiangsu Province(No.CXLX_0101)
文摘Aiming at the problem of poor observability of measurement information in the loosely-coupled integration of the inertial navigation system （INS） and the wireless sensor network （WSN）, this paper presents a tightly-coupled integration based on the Kalman filter （KF）. When the WSN is available, the difference between the distances from the blind node（BN） to the reference nodes （RNs） measured by the INS and those measured by the WSN are used as measurement information for the KF due to its better observability and independence, which can effectively improve the accuracy of the KF. Simulations show that the proposed approach reduces the mean error of the position by about 50% compared with loosely-coupled integration, while the mean error of the velocity is a little higher than that of loosely-coupled integration.
基金supported in part by the National Natural Science Foundation of China(No.41876222)。
文摘In view of the failure of GNSS signals,this paper proposes an INS/GNSS integrated navigation method based on the recurrent neural network(RNN).This proposed method utilizes the calculation principle of INS and the memory function of the RNN to estimate the errors of the INS,thereby obtaining a continuous,reliable and high-precision navigation solution.The performance of the proposed method is firstly demonstrated using an INS/GNSS simulation environment.Subsequently,an experimental test on boat is also conducted to validate the performance of the method.The results show a promising application prospect for RNN in the field of positioning for INS/GNSS integrated navigation in the absence of GNSS signal,as it outperforms extreme learning machine(ELM)and EKF by approximately 30%and 60%,respectively.
文摘The interest for land navigation has increased for the recent years. With the advent of the Global Position System (GPS) we have now the ability to determine the absolute position anywhere on the globe. The problem is that the GPS systems work well only in open environments with no overhead obstructions and they are subject to large unavoidable errors when the reception from some of the satellites are blocked. This occurs frequently in urban environments, forests and tunnels. GPS systems require at least four “visible” satellites to maintain a good position fix. In many situations in which higher level of accuracy is required, the navigation cannot be achieved by GPS alone. This paper discusses the design of a reliable multisensor fusion algorithm using GPS and Inertial Navigation System in order to decrease the implementation cost of such systems on land vehicles. The major contribution of this paper is in the definition of the possible developments and research axes in land navigation.
基金The MKE(the Ministry of Knowledge Economy),Korea,under the ITRC(Information Technology Research Center)support program supervised by the NIPA(National IT Industry Promotion Agency) (NIPA-2012-H0301-12-2006)
文摘This paper proposes a technique that global positioning system(GPS)combines inertial navigation system(INS)by using unscented particle filter(UPF)to estimate the exact outdoor position.This system can make up for the weak point on position estimation by the merits of GPS and INS.In general,extended Kalman filter(EKF)has been widely used in order to combine GPS with INS.However,UPF can get the position more accurately and correctly than EKF when it is applied to real-system included non-linear,irregular distribution errors.In this paper,the accuracy of UPF is proved through the simulation experiment,using the virtual-data needed for the test.
