An interval Kalman filter (IKF) algorithm based on the interval conditional expectation is applied to an integrated global positioning system/inertial navigation system (GPS/INS). Because the IKF algorithm is applica...An interval Kalman filter (IKF) algorithm based on the interval conditional expectation is applied to an integrated global positioning system/inertial navigation system (GPS/INS). Because the IKF algorithm is applicable only to linear interval systems, the extended interval Kalman filter (EIKF) algorithm for non linear integrated systems is developed. A high dynamic aircraft trajectory is designed to test the algorithm developed. The results of computer simulation indicate that the EIKF algorithm is consistent with the traditional SKF scheme, and is also effective for uncertain non linear integrated system.展开更多
A fast U D factorization based Kalman filter for the 21 state integrated global positioning system and inertial navigation system (GPS/INS) is developed from the point of engineering implementation. The conventio...A fast U D factorization based Kalman filter for the 21 state integrated global positioning system and inertial navigation system (GPS/INS) is developed from the point of engineering implementation. The conventional Kalman filter is widely used for integration of GPS/INS, however, due to the model and numerical computation errors, the Kalman filter may diverge in engineering implementation. In order to solve this problem, an extended Kalman filter based on the U D factorization is proposed. Moreover, the high order integrated system suffers from the problem of long computation time, leading to difficulties in real time applications. An algorithmic approach is developed to improve the computational speed. A typical aircraft trajectory is simulated to compare the improvement in the computational speed and the navigation accuracy using the conventional Kalman filter and the fast Kalman filter based on the U D factorization. The results indicate that the methods proposed in this paper are very effective in overcoming these problems for the high dynamic integrated GPS/INS system.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
In inertial navigation system(INS) and global positioning system(GPS) integrated system, GPS antennas are usually not located at the same location as the inertial measurement unit(IMU) of the INS, so the lever arm eff...In inertial navigation system(INS) and global positioning system(GPS) integrated system, GPS antennas are usually not located at the same location as the inertial measurement unit(IMU) of the INS, so the lever arm effect exists, which makes the observation equation highly nonlinear. The INS/GPS integration with constant lever arm effect is studied. The position relation of IMU and GPS's antenna is represented in the earth centered earth fixed frame, while the velocity relation of these two systems is represented in local horizontal frame. Due to the small integration time interval of INS, i.e. 0.1 s in this work, the nonlinearity in the INS error equation is trivial, so the linear INS error model is constructed and addressed by Kalman filter's prediction step. On the other hand, the high nonlinearity in the observation equation due to lever arm effect is addressed by unscented Kalman filter's update step to attain higher accuracy and better applicability. Simulation is designed and the performance of the hybrid filter is validated.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
Traditional cubature Kalman filter(CKF)is a preferable tool for the inertial navigation system(INS)/global positioning system(GPS)integration under Gaussian noises.The CKF,however,may provide a significantly biased es...Traditional cubature Kalman filter(CKF)is a preferable tool for the inertial navigation system(INS)/global positioning system(GPS)integration under Gaussian noises.The CKF,however,may provide a significantly biased estimate when the INS/GPS system suffers from complex non-Gaussian disturbances.To address this issue,a robust nonlinear Kalman filter referred to as cubature Kalman filter under minimum error entropy with fiducial points(MEEF-CKF)is proposed.The MEEF-CKF behaves a strong robustness against complex nonGaussian noises by operating several major steps,i.e.,regression model construction,robust state estimation and free parameters optimization.More concretely,a regression model is constructed with the consideration of residual error caused by linearizing a nonlinear function at the first step.The MEEF-CKF is then developed by solving an optimization problem based on minimum error entropy with fiducial points(MEEF)under the framework of the regression model.In the MEEF-CKF,a novel optimization approach is provided for the purpose of determining free parameters adaptively.In addition,the computational complexity and convergence analyses of the MEEF-CKF are conducted for demonstrating the calculational burden and convergence characteristic.The enhanced robustness of the MEEF-CKF is demonstrated by Monte Carlo simulations on the application of a target tracking with INS/GPS integration under complex nonGaussian noises.展开更多
This Inertial Navigation System (INS), Global Positioning System (GPS) and fluxgate magnetometer technologies have been widely used in a variety of positioning and navigation applications. In this paper, a low cost so...This Inertial Navigation System (INS), Global Positioning System (GPS) and fluxgate magnetometer technologies have been widely used in a variety of positioning and navigation applications. In this paper, a low cost solid state INS/GPS/Magnetometer integrated navigation system has been developed that incorporates measurements from an Inertial Navigation System (INS), Global Positioning System (GPS) and fluxgate magnetometer (Mag.) to provide a reliable complete navigation solution at a high output rate. The body attitude estimates, especially the heading angle, are fundamental challenges in a navigation system. Therefore targeting accurate attitude estimation is considered a significant contribution to the overall navigation error. A better estimation of the body attitude estimates leads to more accurate position and velocity estimation. For that end, the aim of this research is to exploit the magnetometer and accelerometer data in the attitude estimation technique. In this paper, a Scaled Unscented Kalman Filter (SUKF) based on the quaternion concept is designed for the INS/GPS/Mag integrated navigation system under large attitude error conditions. Simulation and experimental results indicate a satisfactory performance of the newly developed model.展开更多
In this paper,the Global Positioning System(GPS)interferometer provides the preliminarily computed quaternions,which are then employed as the measurement of the extended Kalman filter(EKF)for the attitude determinatio...In this paper,the Global Positioning System(GPS)interferometer provides the preliminarily computed quaternions,which are then employed as the measurement of the extended Kalman filter(EKF)for the attitude determination system.The estimated quaternion elements from the EKF output with noticeably improved precision can be converted to the Euler angles for navigation applications.The aim of the study is twofold.Firstly,the GPS-based computed quaternion vector is utilized to avoid the singularity problem.Secondly,the quaternion estimator based on the EKF is adopted to improve the estimation accuracy.Determination of the unknown baseline vector between the antennas sits at the heart of GPS-based attitude determination.Although utilization of the carrier phase observables enables the relative positioning to achieve centimeter level accuracy,however,the quaternion elements derived from the GPS interferometer are inherently noisy.This is due to the fact that the baseline vectors estimated by the least-squares method are based on the raw double-differenced measurements.Construction of the transformation matrix is accessible according to the estimate of baseline vectors and thereafter the computed quaternion elements can be derived.Using the Euler angle method,the process becomes meaningless when the angles are at 90where the singularity problem occurs.A good alternative is the quaternion approach,which possesses advantages over the equivalent Euler angle based transformation since they apply to all attitudes.Simulation results on the attitude estimation performance based on the proposed method will be presented and compared to the conventional method.The results presented in this paper elucidate the superiority of proposed algorithm.展开更多
文摘An interval Kalman filter (IKF) algorithm based on the interval conditional expectation is applied to an integrated global positioning system/inertial navigation system (GPS/INS). Because the IKF algorithm is applicable only to linear interval systems, the extended interval Kalman filter (EIKF) algorithm for non linear integrated systems is developed. A high dynamic aircraft trajectory is designed to test the algorithm developed. The results of computer simulation indicate that the EIKF algorithm is consistent with the traditional SKF scheme, and is also effective for uncertain non linear integrated system.
文摘A fast U D factorization based Kalman filter for the 21 state integrated global positioning system and inertial navigation system (GPS/INS) is developed from the point of engineering implementation. The conventional Kalman filter is widely used for integration of GPS/INS, however, due to the model and numerical computation errors, the Kalman filter may diverge in engineering implementation. In order to solve this problem, an extended Kalman filter based on the U D factorization is proposed. Moreover, the high order integrated system suffers from the problem of long computation time, leading to difficulties in real time applications. An algorithmic approach is developed to improve the computational speed. A typical aircraft trajectory is simulated to compare the improvement in the computational speed and the navigation accuracy using the conventional Kalman filter and the fast Kalman filter based on the U D factorization. The results indicate that the methods proposed in this paper are very effective in overcoming these problems for the high dynamic integrated GPS/INS system.
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.
基金Project(41374018)supported by the National Natural Science Foundation of ChinaProject(J13LN74)supported by the Shandong Province Higher Educational Science and Technology Program,China
文摘In inertial navigation system(INS) and global positioning system(GPS) integrated system, GPS antennas are usually not located at the same location as the inertial measurement unit(IMU) of the INS, so the lever arm effect exists, which makes the observation equation highly nonlinear. The INS/GPS integration with constant lever arm effect is studied. The position relation of IMU and GPS's antenna is represented in the earth centered earth fixed frame, while the velocity relation of these two systems is represented in local horizontal frame. Due to the small integration time interval of INS, i.e. 0.1 s in this work, the nonlinearity in the INS error equation is trivial, so the linear INS error model is constructed and addressed by Kalman filter's prediction step. On the other hand, the high nonlinearity in the observation equation due to lever arm effect is addressed by unscented Kalman filter's update step to attain higher accuracy and better applicability. Simulation is designed and the performance of the hybrid filter is validated.
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.
基金supported by the Fundamental Research Funds for the Central Universities(xzy022020045)the National Natural Science Foundation of China(61976175)。
文摘Traditional cubature Kalman filter(CKF)is a preferable tool for the inertial navigation system(INS)/global positioning system(GPS)integration under Gaussian noises.The CKF,however,may provide a significantly biased estimate when the INS/GPS system suffers from complex non-Gaussian disturbances.To address this issue,a robust nonlinear Kalman filter referred to as cubature Kalman filter under minimum error entropy with fiducial points(MEEF-CKF)is proposed.The MEEF-CKF behaves a strong robustness against complex nonGaussian noises by operating several major steps,i.e.,regression model construction,robust state estimation and free parameters optimization.More concretely,a regression model is constructed with the consideration of residual error caused by linearizing a nonlinear function at the first step.The MEEF-CKF is then developed by solving an optimization problem based on minimum error entropy with fiducial points(MEEF)under the framework of the regression model.In the MEEF-CKF,a novel optimization approach is provided for the purpose of determining free parameters adaptively.In addition,the computational complexity and convergence analyses of the MEEF-CKF are conducted for demonstrating the calculational burden and convergence characteristic.The enhanced robustness of the MEEF-CKF is demonstrated by Monte Carlo simulations on the application of a target tracking with INS/GPS integration under complex nonGaussian noises.
文摘This Inertial Navigation System (INS), Global Positioning System (GPS) and fluxgate magnetometer technologies have been widely used in a variety of positioning and navigation applications. In this paper, a low cost solid state INS/GPS/Magnetometer integrated navigation system has been developed that incorporates measurements from an Inertial Navigation System (INS), Global Positioning System (GPS) and fluxgate magnetometer (Mag.) to provide a reliable complete navigation solution at a high output rate. The body attitude estimates, especially the heading angle, are fundamental challenges in a navigation system. Therefore targeting accurate attitude estimation is considered a significant contribution to the overall navigation error. A better estimation of the body attitude estimates leads to more accurate position and velocity estimation. For that end, the aim of this research is to exploit the magnetometer and accelerometer data in the attitude estimation technique. In this paper, a Scaled Unscented Kalman Filter (SUKF) based on the quaternion concept is designed for the INS/GPS/Mag integrated navigation system under large attitude error conditions. Simulation and experimental results indicate a satisfactory performance of the newly developed model.
基金the Ministry of Science and Technology of the Republic of China[Grant No.MOST 108-2221-E-019-013].
文摘In this paper,the Global Positioning System(GPS)interferometer provides the preliminarily computed quaternions,which are then employed as the measurement of the extended Kalman filter(EKF)for the attitude determination system.The estimated quaternion elements from the EKF output with noticeably improved precision can be converted to the Euler angles for navigation applications.The aim of the study is twofold.Firstly,the GPS-based computed quaternion vector is utilized to avoid the singularity problem.Secondly,the quaternion estimator based on the EKF is adopted to improve the estimation accuracy.Determination of the unknown baseline vector between the antennas sits at the heart of GPS-based attitude determination.Although utilization of the carrier phase observables enables the relative positioning to achieve centimeter level accuracy,however,the quaternion elements derived from the GPS interferometer are inherently noisy.This is due to the fact that the baseline vectors estimated by the least-squares method are based on the raw double-differenced measurements.Construction of the transformation matrix is accessible according to the estimate of baseline vectors and thereafter the computed quaternion elements can be derived.Using the Euler angle method,the process becomes meaningless when the angles are at 90where the singularity problem occurs.A good alternative is the quaternion approach,which possesses advantages over the equivalent Euler angle based transformation since they apply to all attitudes.Simulation results on the attitude estimation performance based on the proposed method will be presented and compared to the conventional method.The results presented in this paper elucidate the superiority of proposed algorithm.