The transfer alignment problem of the shipborne weapon inertial navigation system （INS） is addressed. Specifically, two transfer alignment algorithms subjected to the ship motions induced by the waves are discussed....The transfer alignment problem of the shipborne weapon inertial navigation system （INS） is addressed. Specifically, two transfer alignment algorithms subjected to the ship motions induced by the waves are discussed. To consider the limited maneuver level performed by the ship, a new filter algorithm for transfer alignment methods using velocity and angular rate matching is first derived. And then an improved method using integrated velocity and integrated angular rate matching is introduced to reduce the effect of the ship body flexure. The simulation results show the feasibility and validity of the proposed transfer alignment algorithms.展开更多
A marine INS/GPS adaptive navigation system is presented. GPS with two antenna providing vessel＇ s altitude is selected as the auxiliary system fusing with INS to improve the performance of the hybrid system. The Kal...A marine INS/GPS adaptive navigation system is presented. GPS with two antenna providing vessel＇ s altitude is selected as the auxiliary system fusing with INS to improve the performance of the hybrid system. The Kalman filter is the most frequently used algorithm in the integrated navigation system, which is capable of estimating INS errors online based on the measured errors between INS and GPS. The standard Kalman filter （SKF） assumes that the statistics of the noise on each sensor are given. As long as the noise distributions do not change, the Kalman filter will give the optimal estimation. However GPS receiver will be disturbed easily and thus temporally changing measurement noise will join into the outputs of GPS, which will lead to performance degradation of the Kalman filter. Many researchers introduce fuzzy logic control method into innovation-based adaptive estimation adaptive Kalman filtering （IAE-AKF） algorithm, and accordingly propose various adaptive Kalman filters. However how to design the fuzzy logic controller is a very complicated problem still without a convincing solution. A novel IAE-AKF is proposed herein, which is based on the maximum likelihood criterion for the proper computation of the filter innovation covariance and hence of the filter gain. The approach is direct and simple without having to establish fuzzy inference rules. After having deduced the proposed IAEAKF algorithm theoretically in detail, the approach is tested by the simulation based on the system error model of the developed INS/GPS integrated marine navigation system. Simulation results show that the adaptive Kalman filter outperforms the SKF with higher accuracy, robustness and less computation. It is demonstra- ted that this proposed approach is a valid solution for the unknown changing measurement noise exited in the Kalman filter.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
A new nonlinear algorithm is proposed for strapdown inertial navigation system （SINS）/celestial navigation system （CNS）/global positioning system （GPS） integrated navigation systems. The algorithm employs a nonl...A new nonlinear algorithm is proposed for strapdown inertial navigation system （SINS）/celestial navigation system （CNS）/global positioning system （GPS） integrated navigation systems. The algorithm employs a nonlinear system error model which can be modified by unscented Kalman filter （UKF） to give predictions of local filters. And these predictions can be fused by the federated Kalman filter. In the system error model, the rotation vector is introduced to denote vehicle＇s attitude and has less variables than the quaternion. Also, the UKF method is simplified to estimate the system error model, which can both lead to less calculation and reduce algorithm implement time. In the information fusion section, a modified federated Kalman filter is proposed to solve the singular covariance problem. Specifically, the new algorithm is applied to maneuvering vehicles, and simulation results show that this algorithm is more accurate than the linear integrated navigation algorithm.展开更多
Strapdown inertial navigation system(SINS)/celestial navigation system(CNS)integrated navigation is widely used to achieve long-time and high-precision autonomous navigation for aircraft.In general,SINS/CNS integrated...Strapdown inertial navigation system(SINS)/celestial navigation system(CNS)integrated navigation is widely used to achieve long-time and high-precision autonomous navigation for aircraft.In general,SINS/CNS integrated navigation can be divided into two integrated modes:loosely coupled integrated navigation and tightly coupled integrated navigation.Because the loosely coupled SINS/CNS integrated system is only available in the condition of at least three stars,the latter one is becoming a research hotspot.One major challenge of SINS/CNS integrated navigation is obtaining a high-precision horizon reference.To solve this problem,an innovative tightly coupled rotational SINS/CNS integrated navigation method is proposed.In this method,the rotational SINS error equation in the navigation frame is used as the state model,and the starlight vector and star altitude are used as measurements.Semi-physical simulations are conducted to test the performance of this integrated method.Results show that this tightly coupled rotational SINS/CNS method has the best navigation accuracy compared with SINS,rotational SINS,and traditional tightly coupled SINS/CNS integrated navigation method.展开更多
Transfer alignment is an effective alignment method for the strapdown inertial navigation system （SINS） of airborne weapon systems. The traditional transfer alignment methods for large misalignment angles alignment ...Transfer alignment is an effective alignment method for the strapdown inertial navigation system （SINS） of airborne weapon systems. The traditional transfer alignment methods for large misalignment angles alignment use nonlinear transfer align- ment models and incorporate nonlinear filtering. A rapid transfer alignment method with linear models and linear filtering for ar- bitrary misalignment angles is presented. Through the attitude quaternion decomposition, the purpose of transfer alignment is converted to estimate a constant quaternion. Employing special manipulations on measurement equation, velocity and attitude linear measurement equations are derived. Then the linear trans- fer alignment model for arbitrary misalignment angles is built. An adaptive Kalman filter is developed to handle modeling errors of the measurement noise statistics. Simulation results show feasibili- ty and effectiveness of the proposed method, which provides an alternative rapid transfer alignment method for airborne weapons.展开更多
Gravity-aided inertial navigation is a hot issue in the applications of underwater autonomous vehicle(UAV). Since the matching process is conducted with a gravity anomaly database tabulated in the form of a digital mo...Gravity-aided inertial navigation is a hot issue in the applications of underwater autonomous vehicle(UAV). Since the matching process is conducted with a gravity anomaly database tabulated in the form of a digital model and the resolution is 2’ × 2’,a filter model based on vehicle position is derived and the particularity of inertial navigation system(INS) output is employed to estimate a parameter in the system model. Meanwhile, the matching algorithm based on point mass filter(PMF) is applied and several optimal selection strategies are discussed. It is obtained that the point mass filter algorithm based on the deterministic resampling method has better practicability. The reliability and the accuracy of the algorithm are verified via simulation tests.展开更多
Traditional strapdown inertial navigation system （SINS） algorithm studies are based on ideal measurements from gy- ros and accelerometers, while in the actual strapdown inertial measurement unit （SIMU）, time-async...Traditional strapdown inertial navigation system （SINS） algorithm studies are based on ideal measurements from gy- ros and accelerometers, while in the actual strapdown inertial measurement unit （SIMU）, time-asynchrony between each iner- tial sensor is inevitable. Testing principles and methods for time- asynchrony parameter identification are studied. Under the single- axis swaying environment, the relationships between the SINS platform drift rate and the gyro time-asynchrony are derived using the SINS attitude error equation. It is found that the gyro time- asynchrony error can be considered as a kind of pseudo-coning motion error caused by data processing. After gyro testing and synchronization, the single-axis tumble test method is introduced for the testing of each accelerometer time-asynchrony with respect to the ideal gyro triad. Accelerometer time-asynchrony parame- ter identification models are established using SINS specific force equation. Finally, all of the relative time-asynchrony parameters between inertial sensors are well identified by using fiber optic gyro SIMU as experimental verification.展开更多
基金supported by the Weapon Equipment Research Foundation in Advance(514090909HT0141).
文摘The transfer alignment problem of the shipborne weapon inertial navigation system （INS） is addressed. Specifically, two transfer alignment algorithms subjected to the ship motions induced by the waves are discussed. To consider the limited maneuver level performed by the ship, a new filter algorithm for transfer alignment methods using velocity and angular rate matching is first derived. And then an improved method using integrated velocity and integrated angular rate matching is introduced to reduce the effect of the ship body flexure. The simulation results show the feasibility and validity of the proposed transfer alignment algorithms.
基金This project was supported by the National Natural Science Foundation of China (40125013 &40376011)
文摘A marine INS/GPS adaptive navigation system is presented. GPS with two antenna providing vessel＇ s altitude is selected as the auxiliary system fusing with INS to improve the performance of the hybrid system. The Kalman filter is the most frequently used algorithm in the integrated navigation system, which is capable of estimating INS errors online based on the measured errors between INS and GPS. The standard Kalman filter （SKF） assumes that the statistics of the noise on each sensor are given. As long as the noise distributions do not change, the Kalman filter will give the optimal estimation. However GPS receiver will be disturbed easily and thus temporally changing measurement noise will join into the outputs of GPS, which will lead to performance degradation of the Kalman filter. Many researchers introduce fuzzy logic control method into innovation-based adaptive estimation adaptive Kalman filtering （IAE-AKF） algorithm, and accordingly propose various adaptive Kalman filters. However how to design the fuzzy logic controller is a very complicated problem still without a convincing solution. A novel IAE-AKF is proposed herein, which is based on the maximum likelihood criterion for the proper computation of the filter innovation covariance and hence of the filter gain. The approach is direct and simple without having to establish fuzzy inference rules. After having deduced the proposed IAEAKF algorithm theoretically in detail, the approach is tested by the simulation based on the system error model of the developed INS/GPS integrated marine navigation system. Simulation results show that the adaptive Kalman filter outperforms the SKF with higher accuracy, robustness and less computation. It is demonstra- ted that this proposed approach is a valid solution for the unknown changing measurement noise exited in the Kalman filter.
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supported by the National Natural Science Foundation of China (60535010)
文摘A new nonlinear algorithm is proposed for strapdown inertial navigation system （SINS）/celestial navigation system （CNS）/global positioning system （GPS） integrated navigation systems. The algorithm employs a nonlinear system error model which can be modified by unscented Kalman filter （UKF） to give predictions of local filters. And these predictions can be fused by the federated Kalman filter. In the system error model, the rotation vector is introduced to denote vehicle＇s attitude and has less variables than the quaternion. Also, the UKF method is simplified to estimate the system error model, which can both lead to less calculation and reduce algorithm implement time. In the information fusion section, a modified federated Kalman filter is proposed to solve the singular covariance problem. Specifically, the new algorithm is applied to maneuvering vehicles, and simulation results show that this algorithm is more accurate than the linear integrated navigation algorithm.
基金supported by the National Natural Science Foundation of China(61722301)
文摘Strapdown inertial navigation system(SINS)/celestial navigation system(CNS)integrated navigation is widely used to achieve long-time and high-precision autonomous navigation for aircraft.In general,SINS/CNS integrated navigation can be divided into two integrated modes:loosely coupled integrated navigation and tightly coupled integrated navigation.Because the loosely coupled SINS/CNS integrated system is only available in the condition of at least three stars,the latter one is becoming a research hotspot.One major challenge of SINS/CNS integrated navigation is obtaining a high-precision horizon reference.To solve this problem,an innovative tightly coupled rotational SINS/CNS integrated navigation method is proposed.In this method,the rotational SINS error equation in the navigation frame is used as the state model,and the starlight vector and star altitude are used as measurements.Semi-physical simulations are conducted to test the performance of this integrated method.Results show that this tightly coupled rotational SINS/CNS method has the best navigation accuracy compared with SINS,rotational SINS,and traditional tightly coupled SINS/CNS integrated navigation method.
基金supported by the National Natural Science Foundation of China(61233005)
文摘Transfer alignment is an effective alignment method for the strapdown inertial navigation system （SINS） of airborne weapon systems. The traditional transfer alignment methods for large misalignment angles alignment use nonlinear transfer align- ment models and incorporate nonlinear filtering. A rapid transfer alignment method with linear models and linear filtering for ar- bitrary misalignment angles is presented. Through the attitude quaternion decomposition, the purpose of transfer alignment is converted to estimate a constant quaternion. Employing special manipulations on measurement equation, velocity and attitude linear measurement equations are derived. Then the linear trans- fer alignment model for arbitrary misalignment angles is built. An adaptive Kalman filter is developed to handle modeling errors of the measurement noise statistics. Simulation results show feasibili- ty and effectiveness of the proposed method, which provides an alternative rapid transfer alignment method for airborne weapons.
基金supported by the National Natural Science Foundation of China(61673060)the National Key R&D Plan(2016YFB0501700)
文摘Gravity-aided inertial navigation is a hot issue in the applications of underwater autonomous vehicle(UAV). Since the matching process is conducted with a gravity anomaly database tabulated in the form of a digital model and the resolution is 2’ × 2’,a filter model based on vehicle position is derived and the particularity of inertial navigation system(INS) output is employed to estimate a parameter in the system model. Meanwhile, the matching algorithm based on point mass filter(PMF) is applied and several optimal selection strategies are discussed. It is obtained that the point mass filter algorithm based on the deterministic resampling method has better practicability. The reliability and the accuracy of the algorithm are verified via simulation tests.
基金supported by the National Natural Science Foundation of China(61273333)
文摘Traditional strapdown inertial navigation system （SINS） algorithm studies are based on ideal measurements from gy- ros and accelerometers, while in the actual strapdown inertial measurement unit （SIMU）, time-asynchrony between each iner- tial sensor is inevitable. Testing principles and methods for time- asynchrony parameter identification are studied. Under the single- axis swaying environment, the relationships between the SINS platform drift rate and the gyro time-asynchrony are derived using the SINS attitude error equation. It is found that the gyro time- asynchrony error can be considered as a kind of pseudo-coning motion error caused by data processing. After gyro testing and synchronization, the single-axis tumble test method is introduced for the testing of each accelerometer time-asynchrony with respect to the ideal gyro triad. Accelerometer time-asynchrony parame- ter identification models are established using SINS specific force equation. Finally, all of the relative time-asynchrony parameters between inertial sensors are well identified by using fiber optic gyro SIMU as experimental verification.
