This paper presents a novel SINS/IUSBL integration navigation strategy for underwater vehicles.Based on the principle of inverted USBL(IUSBL),a SINS/IUSBL integration navigation system is established,where the USBL de...This paper presents a novel SINS/IUSBL integration navigation strategy for underwater vehicles.Based on the principle of inverted USBL(IUSBL),a SINS/IUSBL integration navigation system is established,where the USBL device and the SINS are both rigidly mounted onboard the underwater vehicle,and fully developed in-house,the integration navigation system will be able to provide the absolute position of the underwater vehicle with a transponder deployed at a known position beforehand.Furthermore,the state error equation and the measurement equation of SINS/IUSBL integration navigation system are derived,the difference between the position calculated by SINS and the absolute position obtained by IUSBL positioning technology is used as the measurement information.The observability of the integration system is analyzed based on the singular value decomposition(SVD)method.Finally,a mathematical simulation is performed to demonstrate the effectiveness of the proposed SINS/IUSBL integration approach,and the observable degrees of the state variables are also analyzed.展开更多
In outdoor environments, GPS is often used for pedestrian navigation by utilizing its signals for position computation, but in indoor or semi-obstructed environments, GPS signals are often unavailable. Therefore, pede...In outdoor environments, GPS is often used for pedestrian navigation by utilizing its signals for position computation, but in indoor or semi-obstructed environments, GPS signals are often unavailable. Therefore, pedestrian navigation for these environments should be realized by the integration of GPS and inertial navigation system （INS）. However, the lowcost INS could induce errors that may result in a large position drift. The problem can be minimized by mounting the sensors on the pedestrian＇s foot, using zero velocity update （ZUPT） method with the standard navigation algorithm to restrict the error growth. However, heading drift still remains despite using ZUPT measurements since the heading error is unobservable. Also, tbot mounted INS suffers from the initialization ambiguity of position and heading from GPS. In this paper, a novel algorithm is developed to mitigate the heading drift problem when using ZUPT. The method uses building lay- out to aid the heading measurement in Kalman filter, and it could also be combined for the initial- ization. The algorithm has been investigated with real field trials using the low cost Microstrain 3DM-GX3-25 inertial sensor, a Leica GS10 GPS receiver and a uBlox EVK-6T GPS receiver. It could be concluded that the proposed method offers a significant improvement in position accuracy for the long period, allowing pedestrian navigation for nearly40 min with mean position error less than 2.8 m. This method also has a considerable effect on the accuracy of the initialization.展开更多
As the core information infrastructure of modern information warfare,the offensive and defensive confrontations of satellite navigation systems have given rise to navigation warfare,which focuses on seizing control of...As the core information infrastructure of modern information warfare,the offensive and defensive confrontations of satellite navigation systems have given rise to navigation warfare,which focuses on seizing control of navigation resources.Based on the space segment,control segment,and user segment of satellite navigation systems,this paper systematically constructs an offensive-defensive technology system for navigation warfare,and deeply analyzes core measures such as signal enhancement and suppression,autonomous navigation and link jamming,anti-jamming reception,and integrated navigation.It extracts key technologies including adaptive nulling antennas,joint filtering,and multi-dimensional combined jamming,and discusses the technical effectiveness of these technologies by incorporating relevant cases.The advantages of navigation warfare stem from multi-segment coordination and technological inte-gration.In the future,the development directions of navigation warfare will focus on three aspects:enhancing satellite capabilities,tackling core technical challenges,and building a multi-dimensional system.展开更多
In multiple Unmanned Aerial Vehicles(UAV)systems,achieving efficient navigation is essential for executing complex tasks and enhancing autonomy.Traditional navigation methods depend on predefined control strategies an...In multiple Unmanned Aerial Vehicles(UAV)systems,achieving efficient navigation is essential for executing complex tasks and enhancing autonomy.Traditional navigation methods depend on predefined control strategies and trajectory planning and often perform poorly in complex environments.To improve the UAV-environment interaction efficiency,this study proposes a multi-UAV integrated navigation algorithm based on Deep Reinforcement Learning(DRL).This algorithm integrates the Inertial Navigation System(INS),Global Navigation Satellite System(GNSS),and Visual Navigation System(VNS)for comprehensive information fusion.Specifically,an improved multi-UAV integrated navigation algorithm called Information Fusion with MultiAgent Deep Deterministic Policy Gradient(IF-MADDPG)was developed.This algorithm enables UAVs to learn collaboratively and optimize their flight trajectories in real time.Through simulations and experiments,test scenarios in GNSS-denied environments were constructed to evaluate the effectiveness of the algorithm.The experimental results demonstrate that the IF-MADDPG algorithm significantly enhances the collaborative navigation capabilities of multiple UAVs in formation maintenance and GNSS-denied environments.Additionally,it has advantages in terms of mission completion time.This study provides a novel approach for efficient collaboration in multi-UAV systems,which significantly improves the robustness and adaptability of navigation systems.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
To improve the navigation accuracy of an autonomous underwater vehicle （AUV）, a novel terrain passive integrated navigation system （TPINS） is presented. According to the characteristics of the underwater environme...To improve the navigation accuracy of an autonomous underwater vehicle （AUV）, a novel terrain passive integrated navigation system （TPINS） is presented. According to the characteristics of the underwater environment and AUV navigation requirements of low cost and high accuracy, a novel TPINS is designed with a configuration of the strapdown inertial navigation system （SINS）, the terrain reference navigation system （TRNS）, the Doppler velocity sonar （DVS）, the magnetic compass and the navigation computer utilizing the unscented Kalman filter （UKF） to fuse the navigation information from various navigation sensors. Linear filter equations for the extended Kalman filter （EKF）, nonlinear filter equations for the UKF and measurement equations of navigation sensors are addressed. It is indicated from the comparable simulation experiments of the EKF and the UKF that AUV navigation precision is improved substantially with the proposed sensors and the UKF when compared to the EKF. The TPINS designed with the proposed sensors and the UKF is effective in reducing AUV navigation position errors and improving the stability and precision of the AUV underwater integrated navigation.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
Real-time and accurate fault detection is essential to enhance the aircraft navigation system’s reliability and safety. The existent detection methods based on analytical model draws back at simultaneously detecting ...Real-time and accurate fault detection is essential to enhance the aircraft navigation system’s reliability and safety. The existent detection methods based on analytical model draws back at simultaneously detecting gradual and sudden faults. On account of this reason, we propose an online detection solution based on non-analytical model. In this article, the navigation system fault detection model is established based on belief rule base （BRB）, where the system measuring residual and its changing rate are used as the inputs of BRB model and the fault detection function as the output. To overcome the drawbacks of current parameter optimization algorithms for BRB and achieve online update, a parameter recursive estimation algorithm is presented for online BRB detection model based on expectation maximization （EM） algorithm. Furthermore, the proposed method is verified by navigation experiment. Experimental results show that the proposed method is able to effectively realize online parameter evaluation in navigation system fault detection model. The output of the detection model can track the fault state very well, and the faults can be diagnosed in real time and accurately. In addition, the detection ability, especially in the probability of false detection, is superior to offline optimization method, and thus the system reliability has great improvement.展开更多
The IMU（inertial measurement unit） error equations in the earth fixed coordinates are introduced firstly. A fading Kalman filtering is simply introduced and its shortcomings are analyzed, then an adaptive filtering ...The IMU（inertial measurement unit） error equations in the earth fixed coordinates are introduced firstly. A fading Kalman filtering is simply introduced and its shortcomings are analyzed, then an adaptive filtering is applied in IMU/GPS integrated navigation system, in which the adaptive factor is replaced by the fading factor. A practical example is given. The resuits prove that the adaptive filter combined with the fading factor is valid and reliable when applied in IMU/GPS integrated navigation system.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
In micro-electro-mechanical system based inertial navigation system（MEMS-INS）/global position system（GPS） integrated navigation systems, there exist unknown disturbances and abnormal measurements. In order to obta...In micro-electro-mechanical system based inertial navigation system（MEMS-INS）/global position system（GPS） integrated navigation systems, there exist unknown disturbances and abnormal measurements. In order to obtain high estimation accuracy and enhance detection sensitivity to faults in measurements, this paper deals with the problem of model-based robust estimation（RE） and fault detection（FD）. A filter gain matrix and a post-filter are designed to obtain a RE and FD algorithm with current measurements, which is different from most of the existing priori filters using measurements in one-step delay. With the designed filter gain matrix, the H-infinity norm of the transfer function from noise inputs to estimation error outputs is limited within a certain range; with the designed post-filter, the residual signal is robust to disturbances but sensitive to faults. Therefore, the algorithm can guarantee small estimation errors in the presence of disturbances and have high sensitivity to faults. The proposed method is evaluated in an integrated navigation system, and the simulation results show that it is more effective in position estimation and fault signal detection than priori RE and FD algorithms.展开更多
In this paper,we simulate,verify,and compare the performance of three classical geomagnetic matching aided navigation algorithms to assess their applicability to hypersonic vehicle navigation.Firstly,we introduce the ...In this paper,we simulate,verify,and compare the performance of three classical geomagnetic matching aided navigation algorithms to assess their applicability to hypersonic vehicle navigation.Firstly,we introduce the various sources of the geomagnetic field.Secondly,we describe the principles and processes of the geomagnetic contour matching(MAGCOM)algorithm,iterative closest contour point(ICCP)algorithm,and Sandia inertial magnetic aided navigation(SIMAN)algorithm.Thirdly,we discuss the principles of inertial/geomagnetic integrated navigation,and propose the state and observation equations of integrated navigation.Finally,we perform a simulation of inertial/geomagnetic integrated navigation on the hypersonic boost-glide vehicle trajectory.The simulation results indicate that the real-time performance of the SIMAN algorithm can be optimized such that the matching accuracy is higher than that of the other two algorithms.At the same time,the SIMAN algorithm can achieve better stability,and though the amount of measurement noise can be larger,it can still achieve good positioning accuracy.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
In order to take full advantage of federated filter in fault-tolerant design of integrated navigation system, the limitation of fault detection algorithm for gradual changing fault detection and the poor fault toleran...In order to take full advantage of federated filter in fault-tolerant design of integrated navigation system, the limitation of fault detection algorithm for gradual changing fault detection and the poor fault tolerance of global optimal fusion algorithm are the key problems to deal with. Based on theoretical analysis of the influencing factors of federated filtering fault tolerance, global fault-tolerant fusion algorithm and information sharing algorithm are proposed based on fuzzy assessment. It achieves intelligent fault-tolerant structure with two-stage and feedback, including real-time fault detection in sub-filters, and fault-tolerant fusion and information sharing in main filter. The simulation results demonstrate that the algorithm can effectively improve fault-tolerant ability and ensure relatively high positioning precision of integrated navigation system when a subsystem having gradual changing fault.展开更多
In detecting system fault algorithms,the false alarm rate and undectect rate generated by residual Chi-square test can affect the stability of filters.The paper proposes a fault detection algorithm based on sequential...In detecting system fault algorithms,the false alarm rate and undectect rate generated by residual Chi-square test can affect the stability of filters.The paper proposes a fault detection algorithm based on sequential residual Chi-square test and applies to fault detection of an integrated navigation system.The simulation result shows that the algorithm can accurately detect the fault information of global positioning system(GPS),eliminate the influence of false alarm and missed detection on filter,and enhance fault tolerance of integrated navigation systems.展开更多
To further improve the performance of UKF(Unscented Kalman Filter) algorithm used in BDS/SINS(BeiDou Navigation Satellite System/Strap down Inertial Navigation System), an improved GM-UKF(Gaussian Mixture Unscented Ka...To further improve the performance of UKF(Unscented Kalman Filter) algorithm used in BDS/SINS(BeiDou Navigation Satellite System/Strap down Inertial Navigation System), an improved GM-UKF(Gaussian Mixture Unscented Kalman Filter) considering non-Gaussian distribution is discussed in this paper. This new algorithm using SVD(Singular Value Decomposition) is proposed to alternative covariance square root calculation in UKF sigma point production. And to end the rapidly increasing number of Gaussian distributions, PDF(Probability Density Function) re-approximation is conducted. In principle this efficiency algorithm proposed here can achieve higher computational speed compared with traditional GM-UKF. And simulation experiment result show that, compared with UKF and GM-UKF algorithm, new algorithm implemented in BDS/SINS tightly integrated navigation system is suitable for handling nonlinear/non-Gaussian integrated navigation position calculation, for its lower computational complexity with high accuracy.展开更多
Inertial navigation system/visual navigation system(INS/VNS) integrated navigation is a commonly used autonomous navigation method for planetary rovers. Since visual measurements are related to the previous and curren...Inertial navigation system/visual navigation system(INS/VNS) integrated navigation is a commonly used autonomous navigation method for planetary rovers. Since visual measurements are related to the previous and current state vectors(position and attitude) of planetary rovers, the performance of the Kalman filter(KF) will be challenged by the time-correlation problem. A state augmentation method, which augments the previous state value to the state vector, is commonly used when dealing with this problem. However, the augmenting of state dimensions will result in an increase in computation load. In this paper, a state dimension reduced INS/VNS integrated navigation method based on coordinates of feature points is presented that utilizes the information obtained through INS/VNS integrated navigation at a previous moment to overcome the time relevance problem and reduce the dimensions of the state vector. Equations of extended Kalman filter(EKF) are used to demonstrate the equivalence of calculated results between the proposed method and traditional state augmented methods. Results of simulation and experimentation indicate that this method has less computational load but similar accuracy when compared with traditional methods.展开更多
This paper explores multiple model adaptive estimation(MMAE) method, and with it, proposes a novel filtering algorithm. The proposed algorithm is an improved Kalman filter— multiple model adaptive estimation unscente...This paper explores multiple model adaptive estimation(MMAE) method, and with it, proposes a novel filtering algorithm. The proposed algorithm is an improved Kalman filter— multiple model adaptive estimation unscented Kalman filter(MMAE-UKF) rather than conventional Kalman filter methods,like the extended Kalman filter(EKF) and the unscented Kalman filter(UKF). UKF is used as a subfilter to obtain the system state estimate in the MMAE method. Single model filter has poor adaptability with uncertain or unknown system parameters,which the improved filtering method can overcome. Meanwhile,this algorithm is used for integrated navigation system of strapdown inertial navigation system(SINS) and celestial navigation system(CNS) by a ballistic missile's motion. The simulation results indicate that the proposed filtering algorithm has better navigation precision, can achieve optimal estimation of system state, and can be more flexible at the cost of increased computational burden.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
For Inertial Navigation System(INS)/Celestial Navigation System(CNS)/Global Navigation Satellite System(GNSS)integrated navigation system of the missile,the performance of data fusion algorithms based on the Cubature ...For Inertial Navigation System(INS)/Celestial Navigation System(CNS)/Global Navigation Satellite System(GNSS)integrated navigation system of the missile,the performance of data fusion algorithms based on the Cubature Kalman Filter(CKF)is seriously degraded when there are non-Gaussian noise and process-modeling errors in the system model.Therefore,a novel method is proposed,which is called Optimal Data Fusion algorithm based on the Adaptive Fading maximum Correntropy generalized high-degree CKF(AFCCKF-ODF).First,the Adaptive Fading maximum Correntropy generalized high-degree CKF(AFCCKF)is proposed and used as the local filter for the INS/GNSS and INS/CNS subsystems to improve the robustness of local state estimation.Then,the local state estimation is fused based on the minimum variance principle and highdegree cubature criterion to get the globally optimal state.Finally,the experimental results verify that the proposed algorithm can significantly improve the robustness of the missile-borne INS/CNS/GNSS integrated navigation system to non-Gaussian noise and process modeling error and obtain the global optimal navigation information.展开更多
基金The author would like to thank the support in part by the National Natural Science Foundation of China(Grant No.51375088)Inertial Technology Key Lab Fund,the Fundamental Research Funds for the Central Universities(2242015R30031,2242018K40065,2242018K40066)the Foundation of Shanghai Key Laboratory of Navigation and Location Based Services.
文摘This paper presents a novel SINS/IUSBL integration navigation strategy for underwater vehicles.Based on the principle of inverted USBL(IUSBL),a SINS/IUSBL integration navigation system is established,where the USBL device and the SINS are both rigidly mounted onboard the underwater vehicle,and fully developed in-house,the integration navigation system will be able to provide the absolute position of the underwater vehicle with a transponder deployed at a known position beforehand.Furthermore,the state error equation and the measurement equation of SINS/IUSBL integration navigation system are derived,the difference between the position calculated by SINS and the absolute position obtained by IUSBL positioning technology is used as the measurement information.The observability of the integration system is analyzed based on the singular value decomposition(SVD)method.Finally,a mathematical simulation is performed to demonstrate the effectiveness of the proposed SINS/IUSBL integration approach,and the observable degrees of the state variables are also analyzed.
文摘In outdoor environments, GPS is often used for pedestrian navigation by utilizing its signals for position computation, but in indoor or semi-obstructed environments, GPS signals are often unavailable. Therefore, pedestrian navigation for these environments should be realized by the integration of GPS and inertial navigation system （INS）. However, the lowcost INS could induce errors that may result in a large position drift. The problem can be minimized by mounting the sensors on the pedestrian＇s foot, using zero velocity update （ZUPT） method with the standard navigation algorithm to restrict the error growth. However, heading drift still remains despite using ZUPT measurements since the heading error is unobservable. Also, tbot mounted INS suffers from the initialization ambiguity of position and heading from GPS. In this paper, a novel algorithm is developed to mitigate the heading drift problem when using ZUPT. The method uses building lay- out to aid the heading measurement in Kalman filter, and it could also be combined for the initial- ization. The algorithm has been investigated with real field trials using the low cost Microstrain 3DM-GX3-25 inertial sensor, a Leica GS10 GPS receiver and a uBlox EVK-6T GPS receiver. It could be concluded that the proposed method offers a significant improvement in position accuracy for the long period, allowing pedestrian navigation for nearly40 min with mean position error less than 2.8 m. This method also has a considerable effect on the accuracy of the initialization.
文摘As the core information infrastructure of modern information warfare,the offensive and defensive confrontations of satellite navigation systems have given rise to navigation warfare,which focuses on seizing control of navigation resources.Based on the space segment,control segment,and user segment of satellite navigation systems,this paper systematically constructs an offensive-defensive technology system for navigation warfare,and deeply analyzes core measures such as signal enhancement and suppression,autonomous navigation and link jamming,anti-jamming reception,and integrated navigation.It extracts key technologies including adaptive nulling antennas,joint filtering,and multi-dimensional combined jamming,and discusses the technical effectiveness of these technologies by incorporating relevant cases.The advantages of navigation warfare stem from multi-segment coordination and technological inte-gration.In the future,the development directions of navigation warfare will focus on three aspects:enhancing satellite capabilities,tackling core technical challenges,and building a multi-dimensional system.
基金co-supported by the National Natural Science Foundation of China(Nos.92371201 and 52192633)the Natural Science Foundation of Shaanxi Province of China(No.2022JC-03)the Aeronautical Science Foundation of China(No.ASFC-20220019070002)。
文摘In multiple Unmanned Aerial Vehicles(UAV)systems,achieving efficient navigation is essential for executing complex tasks and enhancing autonomy.Traditional navigation methods depend on predefined control strategies and trajectory planning and often perform poorly in complex environments.To improve the UAV-environment interaction efficiency,this study proposes a multi-UAV integrated navigation algorithm based on Deep Reinforcement Learning(DRL).This algorithm integrates the Inertial Navigation System(INS),Global Navigation Satellite System(GNSS),and Visual Navigation System(VNS)for comprehensive information fusion.Specifically,an improved multi-UAV integrated navigation algorithm called Information Fusion with MultiAgent Deep Deterministic Policy Gradient(IF-MADDPG)was developed.This algorithm enables UAVs to learn collaboratively and optimize their flight trajectories in real time.Through simulations and experiments,test scenarios in GNSS-denied environments were constructed to evaluate the effectiveness of the algorithm.The experimental results demonstrate that the IF-MADDPG algorithm significantly enhances the collaborative navigation capabilities of multiple UAVs in formation maintenance and GNSS-denied environments.Additionally,it has advantages in terms of mission completion time.This study provides a novel approach for efficient collaboration in multi-UAV systems,which significantly improves the robustness and adaptability of navigation systems.
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.
基金Pre-Research Program of General Armament Department during the11th Five-Year Plan Period (No51309020503)the National Defense Basic Research Program of China (973Program)(No973-61334)+1 种基金the National Natural Science Foundation of China(No50575042)Specialized Research Fund for the Doctoral Program of Higher Education (No20050286026)
文摘To improve the navigation accuracy of an autonomous underwater vehicle （AUV）, a novel terrain passive integrated navigation system （TPINS） is presented. According to the characteristics of the underwater environment and AUV navigation requirements of low cost and high accuracy, a novel TPINS is designed with a configuration of the strapdown inertial navigation system （SINS）, the terrain reference navigation system （TRNS）, the Doppler velocity sonar （DVS）, the magnetic compass and the navigation computer utilizing the unscented Kalman filter （UKF） to fuse the navigation information from various navigation sensors. Linear filter equations for the extended Kalman filter （EKF）, nonlinear filter equations for the UKF and measurement equations of navigation sensors are addressed. It is indicated from the comparable simulation experiments of the EKF and the UKF that AUV navigation precision is improved substantially with the proposed sensors and the UKF when compared to the EKF. The TPINS designed with the proposed sensors and the UKF is effective in reducing AUV navigation position errors and improving the stability and precision of the AUV underwater integrated navigation.
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the National High-tech Research and Development Program of China(No.2011AA7053016)National Natural Science Foundation of China(No.61174030)
文摘Real-time and accurate fault detection is essential to enhance the aircraft navigation system’s reliability and safety. The existent detection methods based on analytical model draws back at simultaneously detecting gradual and sudden faults. On account of this reason, we propose an online detection solution based on non-analytical model. In this article, the navigation system fault detection model is established based on belief rule base （BRB）, where the system measuring residual and its changing rate are used as the inputs of BRB model and the fault detection function as the output. To overcome the drawbacks of current parameter optimization algorithms for BRB and achieve online update, a parameter recursive estimation algorithm is presented for online BRB detection model based on expectation maximization （EM） algorithm. Furthermore, the proposed method is verified by navigation experiment. Experimental results show that the proposed method is able to effectively realize online parameter evaluation in navigation system fault detection model. The output of the detection model can track the fault state very well, and the faults can be diagnosed in real time and accurately. In addition, the detection ability, especially in the probability of false detection, is superior to offline optimization method, and thus the system reliability has great improvement.
基金Supported by the National Natural Science Foundation of China (No.40274002 No.40474001).
文摘The IMU（inertial measurement unit） error equations in the earth fixed coordinates are introduced firstly. A fading Kalman filtering is simply introduced and its shortcomings are analyzed, then an adaptive filtering is applied in IMU/GPS integrated navigation system, in which the adaptive factor is replaced by the fading factor. A practical example is given. The resuits prove that the adaptive filter combined with the fading factor is valid and reliable when applied in IMU/GPS integrated navigation system.
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co-supported by the National Natural Science Foundation of China(No.61153002)the Aeronautical Science Foundation of China(No.20130153002)
文摘In micro-electro-mechanical system based inertial navigation system（MEMS-INS）/global position system（GPS） integrated navigation systems, there exist unknown disturbances and abnormal measurements. In order to obtain high estimation accuracy and enhance detection sensitivity to faults in measurements, this paper deals with the problem of model-based robust estimation（RE） and fault detection（FD）. A filter gain matrix and a post-filter are designed to obtain a RE and FD algorithm with current measurements, which is different from most of the existing priori filters using measurements in one-step delay. With the designed filter gain matrix, the H-infinity norm of the transfer function from noise inputs to estimation error outputs is limited within a certain range; with the designed post-filter, the residual signal is robust to disturbances but sensitive to faults. Therefore, the algorithm can guarantee small estimation errors in the presence of disturbances and have high sensitivity to faults. The proposed method is evaluated in an integrated navigation system, and the simulation results show that it is more effective in position estimation and fault signal detection than priori RE and FD algorithms.
基金supported by the Space Science and Technology Innovation Fund of China(No.2016KC020028)the Fund of China Space Science and Technology(No.2017-HT-XG)。
文摘In this paper,we simulate,verify,and compare the performance of three classical geomagnetic matching aided navigation algorithms to assess their applicability to hypersonic vehicle navigation.Firstly,we introduce the various sources of the geomagnetic field.Secondly,we describe the principles and processes of the geomagnetic contour matching(MAGCOM)algorithm,iterative closest contour point(ICCP)algorithm,and Sandia inertial magnetic aided navigation(SIMAN)algorithm.Thirdly,we discuss the principles of inertial/geomagnetic integrated navigation,and propose the state and observation equations of integrated navigation.Finally,we perform a simulation of inertial/geomagnetic integrated navigation on the hypersonic boost-glide vehicle trajectory.The simulation results indicate that the real-time performance of the SIMAN algorithm can be optimized such that the matching accuracy is higher than that of the other two algorithms.At the same time,the SIMAN algorithm can achieve better stability,and though the amount of measurement noise can be larger,it can still achieve good positioning accuracy.
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.
基金supported by the National Natural Science Foundationof China (60902055)
文摘In order to take full advantage of federated filter in fault-tolerant design of integrated navigation system, the limitation of fault detection algorithm for gradual changing fault detection and the poor fault tolerance of global optimal fusion algorithm are the key problems to deal with. Based on theoretical analysis of the influencing factors of federated filtering fault tolerance, global fault-tolerant fusion algorithm and information sharing algorithm are proposed based on fuzzy assessment. It achieves intelligent fault-tolerant structure with two-stage and feedback, including real-time fault detection in sub-filters, and fault-tolerant fusion and information sharing in main filter. The simulation results demonstrate that the algorithm can effectively improve fault-tolerant ability and ensure relatively high positioning precision of integrated navigation system when a subsystem having gradual changing fault.
基金supported by the National Natural Science Foundation of China(6063403060702066)+1 种基金the Aerospace Science Foundation(20090853013)Fundmental Research Foundation of NWPU(JC201015),Soaring Star of NWPU
文摘In detecting system fault algorithms,the false alarm rate and undectect rate generated by residual Chi-square test can affect the stability of filters.The paper proposes a fault detection algorithm based on sequential residual Chi-square test and applies to fault detection of an integrated navigation system.The simulation result shows that the algorithm can accurately detect the fault information of global positioning system(GPS),eliminate the influence of false alarm and missed detection on filter,and enhance fault tolerance of integrated navigation systems.
基金supported by Chinese National Natural ScienceFoundation (41674016 and 41274016)
文摘To further improve the performance of UKF(Unscented Kalman Filter) algorithm used in BDS/SINS(BeiDou Navigation Satellite System/Strap down Inertial Navigation System), an improved GM-UKF(Gaussian Mixture Unscented Kalman Filter) considering non-Gaussian distribution is discussed in this paper. This new algorithm using SVD(Singular Value Decomposition) is proposed to alternative covariance square root calculation in UKF sigma point production. And to end the rapidly increasing number of Gaussian distributions, PDF(Probability Density Function) re-approximation is conducted. In principle this efficiency algorithm proposed here can achieve higher computational speed compared with traditional GM-UKF. And simulation experiment result show that, compared with UKF and GM-UKF algorithm, new algorithm implemented in BDS/SINS tightly integrated navigation system is suitable for handling nonlinear/non-Gaussian integrated navigation position calculation, for its lower computational complexity with high accuracy.
基金supported by the National Natural Science Foundation of China (Nos. 61233005 and 61503013)the National Basic Research Program of China (No. 2014CB744202)+2 种基金Beijing Youth Talent ProgramFundamental Science on Novel Inertial Instrument & Navigation System Technology LaboratoryProgram for Changjiang Scholars and Innovative Research Team in University (IRT1203) for their valuable comments
文摘Inertial navigation system/visual navigation system(INS/VNS) integrated navigation is a commonly used autonomous navigation method for planetary rovers. Since visual measurements are related to the previous and current state vectors(position and attitude) of planetary rovers, the performance of the Kalman filter(KF) will be challenged by the time-correlation problem. A state augmentation method, which augments the previous state value to the state vector, is commonly used when dealing with this problem. However, the augmenting of state dimensions will result in an increase in computation load. In this paper, a state dimension reduced INS/VNS integrated navigation method based on coordinates of feature points is presented that utilizes the information obtained through INS/VNS integrated navigation at a previous moment to overcome the time relevance problem and reduce the dimensions of the state vector. Equations of extended Kalman filter(EKF) are used to demonstrate the equivalence of calculated results between the proposed method and traditional state augmented methods. Results of simulation and experimentation indicate that this method has less computational load but similar accuracy when compared with traditional methods.
基金supported by the National Basic Research Program of China(973Program)(2014CB744206)
文摘This paper explores multiple model adaptive estimation(MMAE) method, and with it, proposes a novel filtering algorithm. The proposed algorithm is an improved Kalman filter— multiple model adaptive estimation unscented Kalman filter(MMAE-UKF) rather than conventional Kalman filter methods,like the extended Kalman filter(EKF) and the unscented Kalman filter(UKF). UKF is used as a subfilter to obtain the system state estimate in the MMAE method. Single model filter has poor adaptability with uncertain or unknown system parameters,which the improved filtering method can overcome. Meanwhile,this algorithm is used for integrated navigation system of strapdown inertial navigation system(SINS) and celestial navigation system(CNS) by a ballistic missile's motion. The simulation results indicate that the proposed filtering algorithm has better navigation precision, can achieve optimal estimation of system state, and can be more flexible at the cost of increased computational burden.
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.
基金supported by the National Natural Science Foundation of China(Nos.61873064 and 51375087)the Transformation Program of Science and Technology Achievements of Jiangsu Province(No.BA2016139)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX18_0073)。
文摘For Inertial Navigation System(INS)/Celestial Navigation System(CNS)/Global Navigation Satellite System(GNSS)integrated navigation system of the missile,the performance of data fusion algorithms based on the Cubature Kalman Filter(CKF)is seriously degraded when there are non-Gaussian noise and process-modeling errors in the system model.Therefore,a novel method is proposed,which is called Optimal Data Fusion algorithm based on the Adaptive Fading maximum Correntropy generalized high-degree CKF(AFCCKF-ODF).First,the Adaptive Fading maximum Correntropy generalized high-degree CKF(AFCCKF)is proposed and used as the local filter for the INS/GNSS and INS/CNS subsystems to improve the robustness of local state estimation.Then,the local state estimation is fused based on the minimum variance principle and highdegree cubature criterion to get the globally optimal state.Finally,the experimental results verify that the proposed algorithm can significantly improve the robustness of the missile-borne INS/CNS/GNSS integrated navigation system to non-Gaussian noise and process modeling error and obtain the global optimal navigation information.
