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 Successive Orthogonalization Decentralized Kalman Filter (SODKF ) is a new method which is used for large system state estimation. It can be applied not only to large system decentralization, but also to precisi...The Successive Orthogonalization Decentralized Kalman Filter (SODKF ) is a new method which is used for large system state estimation. It can be applied not only to large system decentralization, but also to precision realization at approximately the same level of the global filter, thus, making possible the engineering operation as well as shortening the computing time. This paper discusses the principles and features of SODKF when used in GPS/INS integrated navigation system. The system will be firstly divided into three subsystems and then corrected in both open and closed loops. The system simulation results by two integrated patterns show that SODKF is efficient and realizable. While the three subsystems are simulated in series, the computing speed doubles that of the global system. In addition, its optimal estimating precision remains unchanged. It can be concluded from this paper that large integrated navigation systems with GPS, INS, Terrain Match, Loran C, Doppler Radar and Radio Altimeter can be made more efficient by this multi subsystem of navigation.展开更多
This paper deals with the research of the GPS/INS integrated navigation system applying Extended Kalman Filter, which involves integrated principles, scheme and technology of combining with real INS and GPS receiver d...This paper deals with the research of the GPS/INS integrated navigation system applying Extended Kalman Filter, which involves integrated principles, scheme and technology of combining with real INS and GPS receiver data. Emphases are placed on the modeling of system errors and implementation of the integrated system. Both loose and tightly coupled GPS/INS integrated in schemes are analyzed. On the basis of our experience accumulated in the research of GPS/INS for many years, the GPS/INS integrated navigation developing system is developed. It can be put into efficient and economic use in the study and design of integrated navigation system. It plays an important role in the aeronautical and astronautical fields in China. This system is not only a computer aided design software but also a semi physical simulation system by obtaining real INS and GPS receiver data. So the key software unit of the developing system could be conveniently transferred into practical engineering software in actual hardware integrated system. The application of this system shows that the design ideas and integrated scheme of this development system are successful, and can achieve good navigation result.展开更多
A method of improving the navigation accuracy of strapdown inertial navigation system (SINS) is studied. The particular technique discussed involves the continuous rotation of gyros and accelerometers cluster about th...A method of improving the navigation accuracy of strapdown inertial navigation system (SINS) is studied. The particular technique discussed involves the continuous rotation of gyros and accelerometers cluster about the vertical axis of the vehicle. Then the errors of these sensors will have periodic variation corresponding to components along the body frame. Under this condition, the modulated sensor errors produce reduced system errors. Theoretical analysis based on a new coordinate system defined as sensing frame and test results are presented, and they indicate the method attenuates the navigation errors brought by the gyros' random constant drift and the accelerometer's bias and their white noise compared to the conventional method.展开更多
The principles of the SINS/DVL integrated navigation system are introduced, and the compass status accuracy is compared. When the heading is changed, the dead reckoning algorithm using the heading information of the S...The principles of the SINS/DVL integrated navigation system are introduced, and the compass status accuracy is compared. When the heading is changed, the dead reckoning algorithm using the heading information of the SINS (Strapdown inertial navigation systems) and DVL (doppler velocity log) is adopted to substitute the SINS/DVL integrated system. The simulation results show that the method can improve the accuracy of integrated navigation system when AUV (autonomous underwater vehicle) is in motion.展开更多
A new adaptive federal Kalman filter for a strapdown integrated navigation system/global positioning system (SINS/GPS) is given. The developed federal Kalman filter is based on the trace operation of parameters estima...A new adaptive federal Kalman filter for a strapdown integrated navigation system/global positioning system (SINS/GPS) is given. The developed federal Kalman filter is based on the trace operation of parameters estimation's error covariance matrix and the spectral radius of update measurement noise variance-covariance matrix for the proper choice of the filter weight and hence the filter gain factors. Theoretical analysis and results from simulation in which the SINS/GPS was compared to conventional Kalman filter are presented. Results show that the algorithm of this adaptive federal Kalman filter is simpler than that of the conventional one. Furthermore, it outperforms the conventional Kalman filter when the system is undertaken measurement malfunctions because of its possession of adaptive ability. This filter can be used in the vehicle integrated navigation system.展开更多
Aiming at the problem that the traditional Unscented Kalman Filtering(UKF) algorithm can't solve the problem that the measurement covariance matrix is unknown and the measured value contains outliers,this paper pr...Aiming at the problem that the traditional Unscented Kalman Filtering(UKF) algorithm can't solve the problem that the measurement covariance matrix is unknown and the measured value contains outliers,this paper proposes a robust adaptive UKF algorithm based on Support Vector Regression(SVR).The algorithm combines the advantages of support vector regression with small samples,nonlinear learning ability and online estimation capability of adaptive algorithm based on innovation.Firstly,the SVR model is trained by using the innovation in the sliding window,and the new innovation is monitored.If the deviation between the estimated innovation and the measured innovation exceeds a given threshold,then measured innovation will be replaced by the predicted innovation,and then the processed innovation is used to calculate the measurement noise covariance matrix using the adaptive estimation algorithm.Simulation experiments and measured data experiments show that SVRUKF is significantly better than the traditional UKF,robust UKF and adaptive UKF algorithms for the case where the covariance matrix is unknown and the measured values have outliers.展开更多
GPS (Global Positioning System) has been widely used in car navigation systems. Most car navigation systems estimate the car position from GPS and DR (dead reckoning). However, the unknown GPS noise characteristic and...GPS (Global Positioning System) has been widely used in car navigation systems. Most car navigation systems estimate the car position from GPS and DR (dead reckoning). However, the unknown GPS noise characteristic and the unbounded DR accumulation of errors over time make the position information with undesirable position errors. The map matching can improve the position accuracy and availability of the vehicular position system. In this paper, general principle of map matching is investigated according to segmentation and feature extraction, and a map matching algorithm based on D-S (Dempster-Shafer) evidence reasoning for GPS integrated navigation system is proposed, which can find the exact road on which a car moves. For the experiments, a car navigation system is developed with some sensors and the field test demonstrates the effectiveness and applicability of the algorithm for the car location and navigation.展开更多
Inertial/gravity matching integrated navigation system can effectively improve the longendurance navigation ability of underwater vehicles.Through the analysis of the matching process,the problem of unequal-interval i...Inertial/gravity matching integrated navigation system can effectively improve the longendurance navigation ability of underwater vehicles.Through the analysis of the matching process,the problem of unequal-interval in matching trajectory is addressed by an unequal-interval data fusion algorithm which is based on the unequal-interval characteristics analysis of the matching trajectory.Compared with previously available methods,the proposed algorithm improves the location precision.In conclusion,simulations of the integrated navigation system demonstrated the effectiveness and superiority of the proposed algorithm.展开更多
To deal with the low location accuracy issue of existing underwater navigation technologies in autonomous underwater vehicles(AUVs),a distributed fusion algorithm which combines the model's analysis method with a ...To deal with the low location accuracy issue of existing underwater navigation technologies in autonomous underwater vehicles(AUVs),a distributed fusion algorithm which combines the model's analysis method with a multi-scale transformation method is proposed for integrated navigation system based on AUV.First,integrated navigation system theory and system error sources are introduced in details.Secondly,a navigation system's observation equation on the original scale is decomposed into different scales by the discrete wavelet transform method,and noise reduction is performed by setting the wavelet de-noising threshold.At last,the dynamic equation and observation equations are fused on different scales by the wavelet transformation and Kalman filter.The results show that the proposed algorithm has smaller navigation error and higher navigation accuracy.展开更多
在室内导航定位中,射频识别(Radio Frequency Identification,RFID)技术具有信号穿透性强、成本低廉等诸多优点,能够有效代替GPS完成室内组合导航。针对室内惯性导航误差发散和滤波中噪声参数不确定的问题,提出了基于自适应卡尔曼滤波(A...在室内导航定位中,射频识别(Radio Frequency Identification,RFID)技术具有信号穿透性强、成本低廉等诸多优点,能够有效代替GPS完成室内组合导航。针对室内惯性导航误差发散和滤波中噪声参数不确定的问题,提出了基于自适应卡尔曼滤波(Adaptive Kalman Filtering,AKF)的RFID/SINS组合导航系统,通过RFID定位系统抑制惯性导航误差发散,并应用AKF将噪声参数与量测输出参数关联实现实时更新。对AKF和标准卡尔曼滤波(Kalman Filtering,KF)下的RFID/SINS组合导航系统进行了仿真和实验。结果表明,在AKF下组合导航系统平均定位误差降低了10%,位置稳定性提升了7.4%,定位误差保持在0.07 m左右。基于AKF的RFID/SINS组合导航系统能够满足室内高精度定位导航的需求。展开更多
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 Successive Orthogonalization Decentralized Kalman Filter (SODKF ) is a new method which is used for large system state estimation. It can be applied not only to large system decentralization, but also to precision realization at approximately the same level of the global filter, thus, making possible the engineering operation as well as shortening the computing time. This paper discusses the principles and features of SODKF when used in GPS/INS integrated navigation system. The system will be firstly divided into three subsystems and then corrected in both open and closed loops. The system simulation results by two integrated patterns show that SODKF is efficient and realizable. While the three subsystems are simulated in series, the computing speed doubles that of the global system. In addition, its optimal estimating precision remains unchanged. It can be concluded from this paper that large integrated navigation systems with GPS, INS, Terrain Match, Loran C, Doppler Radar and Radio Altimeter can be made more efficient by this multi subsystem of navigation.
文摘This paper deals with the research of the GPS/INS integrated navigation system applying Extended Kalman Filter, which involves integrated principles, scheme and technology of combining with real INS and GPS receiver data. Emphases are placed on the modeling of system errors and implementation of the integrated system. Both loose and tightly coupled GPS/INS integrated in schemes are analyzed. On the basis of our experience accumulated in the research of GPS/INS for many years, the GPS/INS integrated navigation developing system is developed. It can be put into efficient and economic use in the study and design of integrated navigation system. It plays an important role in the aeronautical and astronautical fields in China. This system is not only a computer aided design software but also a semi physical simulation system by obtaining real INS and GPS receiver data. So the key software unit of the developing system could be conveniently transferred into practical engineering software in actual hardware integrated system. The application of this system shows that the design ideas and integrated scheme of this development system are successful, and can achieve good navigation result.
文摘A method of improving the navigation accuracy of strapdown inertial navigation system (SINS) is studied. The particular technique discussed involves the continuous rotation of gyros and accelerometers cluster about the vertical axis of the vehicle. Then the errors of these sensors will have periodic variation corresponding to components along the body frame. Under this condition, the modulated sensor errors produce reduced system errors. Theoretical analysis based on a new coordinate system defined as sensing frame and test results are presented, and they indicate the method attenuates the navigation errors brought by the gyros' random constant drift and the accelerometer's bias and their white noise compared to the conventional method.
文摘The principles of the SINS/DVL integrated navigation system are introduced, and the compass status accuracy is compared. When the heading is changed, the dead reckoning algorithm using the heading information of the SINS (Strapdown inertial navigation systems) and DVL (doppler velocity log) is adopted to substitute the SINS/DVL integrated system. The simulation results show that the method can improve the accuracy of integrated navigation system when AUV (autonomous underwater vehicle) is in motion.
文摘A new adaptive federal Kalman filter for a strapdown integrated navigation system/global positioning system (SINS/GPS) is given. The developed federal Kalman filter is based on the trace operation of parameters estimation's error covariance matrix and the spectral radius of update measurement noise variance-covariance matrix for the proper choice of the filter weight and hence the filter gain factors. Theoretical analysis and results from simulation in which the SINS/GPS was compared to conventional Kalman filter are presented. Results show that the algorithm of this adaptive federal Kalman filter is simpler than that of the conventional one. Furthermore, it outperforms the conventional Kalman filter when the system is undertaken measurement malfunctions because of its possession of adaptive ability. This filter can be used in the vehicle integrated navigation system.
文摘Aiming at the problem that the traditional Unscented Kalman Filtering(UKF) algorithm can't solve the problem that the measurement covariance matrix is unknown and the measured value contains outliers,this paper proposes a robust adaptive UKF algorithm based on Support Vector Regression(SVR).The algorithm combines the advantages of support vector regression with small samples,nonlinear learning ability and online estimation capability of adaptive algorithm based on innovation.Firstly,the SVR model is trained by using the innovation in the sliding window,and the new innovation is monitored.If the deviation between the estimated innovation and the measured innovation exceeds a given threshold,then measured innovation will be replaced by the predicted innovation,and then the processed innovation is used to calculate the measurement noise covariance matrix using the adaptive estimation algorithm.Simulation experiments and measured data experiments show that SVRUKF is significantly better than the traditional UKF,robust UKF and adaptive UKF algorithms for the case where the covariance matrix is unknown and the measured values have outliers.
文摘GPS (Global Positioning System) has been widely used in car navigation systems. Most car navigation systems estimate the car position from GPS and DR (dead reckoning). However, the unknown GPS noise characteristic and the unbounded DR accumulation of errors over time make the position information with undesirable position errors. The map matching can improve the position accuracy and availability of the vehicular position system. In this paper, general principle of map matching is investigated according to segmentation and feature extraction, and a map matching algorithm based on D-S (Dempster-Shafer) evidence reasoning for GPS integrated navigation system is proposed, which can find the exact road on which a car moves. For the experiments, a car navigation system is developed with some sensors and the field test demonstrates the effectiveness and applicability of the algorithm for the car location and navigation.
基金Supported by the National Natural Science Foundation for Outstanding Youth(61422102)Special Fund for Basic Research on Scientific Instruments of the National Natural Science Foundation of China(61127004)
文摘Inertial/gravity matching integrated navigation system can effectively improve the longendurance navigation ability of underwater vehicles.Through the analysis of the matching process,the problem of unequal-interval in matching trajectory is addressed by an unequal-interval data fusion algorithm which is based on the unequal-interval characteristics analysis of the matching trajectory.Compared with previously available methods,the proposed algorithm improves the location precision.In conclusion,simulations of the integrated navigation system demonstrated the effectiveness and superiority of the proposed algorithm.
基金National Natural Science Foundation of China(51779057,51709061,51509057)the Equipment Pre-Research Project(41412030201)the National 863 High Technology Development Plan Project(2011AA09A106)。
文摘To deal with the low location accuracy issue of existing underwater navigation technologies in autonomous underwater vehicles(AUVs),a distributed fusion algorithm which combines the model's analysis method with a multi-scale transformation method is proposed for integrated navigation system based on AUV.First,integrated navigation system theory and system error sources are introduced in details.Secondly,a navigation system's observation equation on the original scale is decomposed into different scales by the discrete wavelet transform method,and noise reduction is performed by setting the wavelet de-noising threshold.At last,the dynamic equation and observation equations are fused on different scales by the wavelet transformation and Kalman filter.The results show that the proposed algorithm has smaller navigation error and higher navigation accuracy.
