Over the past few decades, numerous adaptive Kalman filters(AKFs) have been proposed. However, achieving online estimation with both high estimation accuracy and fast convergence speed is challenging, especially when ...Over the past few decades, numerous adaptive Kalman filters(AKFs) have been proposed. However, achieving online estimation with both high estimation accuracy and fast convergence speed is challenging, especially when both the process noise and measurement noise covariance matrices are relatively inaccurate. Maximum likelihood estimation(MLE) possesses the potential to achieve this goal, since its theoretical accuracy is guaranteed by asymptotic optimality and the convergence speed is fast due to weak dependence on accurate state estimation.Unfortunately, the maximum likelihood cost function is so intricate that the existing MLE methods can only simply ignore all historical measurement information to achieve online estimation,which cannot adequately realize the potential of MLE. In order to design online MLE-based AKFs with high estimation accuracy and fast convergence speed, an online exploratory MLE approach is proposed, based on which a mini-batch coordinate descent noise covariance matrix estimation framework is developed. In this framework, the maximum likelihood cost function is simplified for online estimation with fewer and simpler terms which are selected in a mini-batch and calculated with a backtracking method. This maximum likelihood cost function is sidestepped and solved by exploring possible estimated noise covariance matrices adaptively while the historical measurement information is adequately utilized. Furthermore, four specific algorithms are derived under this framework to meet different practical requirements in terms of convergence speed, estimation accuracy,and calculation load. Abundant simulations and experiments are carried out to verify the validity and superiority of the proposed algorithms as compared with existing state-of-the-art AKFs.展开更多
Solar radio burst(SRB)is one of the main natural interference sources of Global Positioning System(GPS)signals and can reduce the signal-to-noise ratio(SNR),directly affecting the tracking performance of GPS receivers...Solar radio burst(SRB)is one of the main natural interference sources of Global Positioning System(GPS)signals and can reduce the signal-to-noise ratio(SNR),directly affecting the tracking performance of GPS receivers.In this paper,a tracking algorithm based on the adaptive Kalman filter(AKF)with carrier-to-noise ratio estimation is proposed and compared with the conventional second-order phase-locked loop tracking algo-rithms and the improved Sage-Husa adaptive Kalman filter(SHAKF)algorithm.It is discovered that when the SRBs occur,the improved SHAKF and the AKF with carrier-to-noise ratio estimation enable stable tracking to loop signals.The conven-tional second-order phase-locked loop tracking algorithms fail to track the receiver signal.The standard deviation of the carrier phase error of the AKF with carrier-to-noise ratio estimation out-performs 50.51%of the improved SHAKF algorithm,showing less fluctuation and better stability.The proposed algorithm is proven to show more excellent adaptability in the severe envi-ronment caused by the SRB occurrence and has better tracking performance.展开更多
In order to measure the parameters of flight rocket by using radar,rocket impact point was estimated accurately for rocket trajectory correction.The Kalman filter with adaptive filter gain matrix was adopted.According...In order to measure the parameters of flight rocket by using radar,rocket impact point was estimated accurately for rocket trajectory correction.The Kalman filter with adaptive filter gain matrix was adopted.According to the particle trajectory model,the adaptive Kalman filter trajectory model was constructed for removing and filtering the outliers of the parameters during a section of flight detected by three-dimensional data radar and the rocket impact point was extrapolated.The results of numerical simulation show that the outliers and noise in trajectory measurement signal can be removed effectively by using the adaptive Kalman filter and the filter variance can converge in a short period of time.Based on the relation of filtering time and impact point estimation error,choosing the filtering time of 8-10 scan get the minimum estimation error of impact point.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
Pure inertial navigation system(INS) has divergent localization errors after a long time. In order to compensate the disadvantage, wireless sensor network(WSN) associated with the INS was applied to estimate the mobil...Pure inertial navigation system(INS) has divergent localization errors after a long time. In order to compensate the disadvantage, wireless sensor network(WSN) associated with the INS was applied to estimate the mobile target positioning. Taking traditional Kalman filter(KF) as the framework, the system equation of KF was established by the INS and the observation equation of position errors was built by the WSN. Meanwhile, the observation equation of velocity errors was established by the velocity difference between the INS and WSN, then the covariance matrix of Kalman filter measurement noise was adjusted with fuzzy inference system(FIS), and the fuzzy adaptive Kalman filter(FAKF) based on the INS/WSN was proposed. The simulation results show that the FAKF method has better accuracy and robustness than KF and EKF methods and shows good adaptive capacity with time-varying system noise. Finally, experimental results further prove that FAKF has the fast convergence error, in comparison with KF and EKF methods.展开更多
As an orientation measurement system,north-finder has been playing a significant role in both military and civilian fields of orientation and control.In this paper,to deal with drawbacks in the conventional north-find...As an orientation measurement system,north-finder has been playing a significant role in both military and civilian fields of orientation and control.In this paper,to deal with drawbacks in the conventional north-finding systems,a dynamic strategy based on continuous rotation modulation to measure the rotational angular velocity of the earth is proposed.By modeling the dynamic error,optimizing the process constraint and estimating dynamic noise,a method combining delay compensation and hardware adjustment,and a constrained adaptive Kalman filter(CAKF)algorithm are designed for the north-finding strategy.According to simulation and experiments,the proposed algorithm can achieve the high-precision north-finding with robust and anti-noise performance.展开更多
The alignment accuracy of the strap-down inertial navigation system （SINS） of airborne weapon is greatly degraded by the dynamic wing flexure of the aircraft. An adaptive Kalman filter uses innovation sequences base...The alignment accuracy of the strap-down inertial navigation system （SINS） of airborne weapon is greatly degraded by the dynamic wing flexure of the aircraft. An adaptive Kalman filter uses innovation sequences based on the maximum likelihood estimated criterion to adapt the system noise covariance matrix and the measurement noise covariance matrix on line, which is used to estimate the misalignment if the model of wing flexure of the aircraft is unknown. From a number of simulations, it is shown that the accuracy of the adaptive Kalman filter is better than the conventional Kalman filter, and the erroneous misalignment models of the wing flexure of aircraft will cause bad estimation results of Kalman filter using attitude match method.展开更多
Several filter techniques were available for the GPS position estimation problem of maneuvering vehicle ranging from using different process noises to Interactive Multiple Model (IMM). The limitation of using standard...Several filter techniques were available for the GPS position estimation problem of maneuvering vehicle ranging from using different process noises to Interactive Multiple Model (IMM). The limitation of using standard Kalman filters is listed.The performance of proposed adaptive filter is compared with that of the standard ones,two types of dynamic modeling of the maneuvering vehicle are used.The simulation is based on the almanac data of the GPS satellites to compute its feasibility during the simulation time and position on shape 8 track with continuous vehicle maneuvering. The goal is to obtain computationally efficient filter with reasonable accuracy for vehicle in maneuvering situation. The filter proposed is an alternative to the filter proposed in Ref. [1] with low computational burden.展开更多
MEMS(micro-electro-mechanical-system)IMU(inertial measurement unit)sensors are characteristically noisy and this presents a serious problem to their effective use.The Kalman filter assumes zero-mean Gaussian process a...MEMS(micro-electro-mechanical-system)IMU(inertial measurement unit)sensors are characteristically noisy and this presents a serious problem to their effective use.The Kalman filter assumes zero-mean Gaussian process and measurement noise variables,and then recursively computes optimal state estimates.However,establishing the exact noise statistics is a non-trivial task.Additionally,this noise often varies widely in operation.Addressing this challenge is the focus of adaptive Kalman filtering techniques.In the covariance scaling method,the process and measurement noise covariance matrices Q and R are uniformly scaled by a scalar-quantity attenuating window.This study proposes a new approach where individual elements of Q and R are scaled element-wise to ensure more granular adaptation of noise components and hence improve accuracy.In addition,the scaling is performed over a smoothly decreasing window to balance aggressiveness of response and stability in steady state.Experimental results show that the root mean square errors for both pith and roll axes are significantly reduced compared to the conventional noise adaptation method,albeit at a slightly higher computational cost.Specifically,the root mean square pitch errors are 1.1∘under acceleration and 2.1∘under rotation,which are significantly less than the corresponding errors of the adaptive complementary filter and conventional covariance scaling-based adaptive Kalman filter tested under the same conditions.展开更多
In order to improve the accuracy of the battery state of charge（SOC） estimation, in this paper we take a lithiumion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, ...In order to improve the accuracy of the battery state of charge（SOC） estimation, in this paper we take a lithiumion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, the second-order battery system model is introduced. Meanwhile, the temperature and charge rate are introduced into the model. Then, the temperature and the charge rate are adopted to estimate the battery SOC, with the help of the parameters of an adaptive Kalman filter based estimation algorithm model. Afterwards, it is verified by the numerical simulation that in the ideal case, the accuracy of SOC estimation can be enhanced by adding two elements, namely, the temperature and charge rate.Finally, the actual road conditions are simulated with ADVISOR, and the simulation results show that the proposed method improves the accuracy of battery SOC estimation under actual road conditions. Thus, its application scope in engineering is greatly expanded.展开更多
The model of partially observed nonlinear system,called extended Kalman filter(EKF),and depending on some unknown parameters is considered.An approximation of the unobserved component is proposed.This approximation is...The model of partially observed nonlinear system,called extended Kalman filter(EKF),and depending on some unknown parameters is considered.An approximation of the unobserved component is proposed.This approximation is realized in two steps.First a the method of moments estimator of unknown parameter is constructed and then this estimator is substituted in the equations of extended Kalman filter.The obtained equations describe the adaptive extended Kalman filter.The properties of estimator of the unknown parameter and of the unknown state are described in the asymptotic of small noise in observations.展开更多
This article proposes an adaptive extended Kalman filter(EKF)for nonlinear cyber-physical systems(CPSs)under unknown inputs and non-Gaussian noises.It is known that the traditional extended Kalman filter is applicable...This article proposes an adaptive extended Kalman filter(EKF)for nonlinear cyber-physical systems(CPSs)under unknown inputs and non-Gaussian noises.It is known that the traditional extended Kalman filter is applicable to nonlinear systems with Gaussian white noise.The system is reformulated with intermediate variables to expand the application of nonlinear systems under unknown inputs and non-Gaussian noises,which help decompose unknown input estimation into residual tracking and state observation subproblems.By introducing the orthogonal principle of innovation and attenuation factor,the intermediate variables-based filter can improve the estimation performance under non-Gaussian noises and unknown inputs.Simulation results validate the effectiveness of the proposed method.展开更多
Beidou-3 navigation satellite system(BDS-3)initiated a real-time service for precise point positioning(PPP)using the B2b signal,mainly for users in China and surrounding areas.In this paper,the performance of PPP-B2b ...Beidou-3 navigation satellite system(BDS-3)initiated a real-time service for precise point positioning(PPP)using the B2b signal,mainly for users in China and surrounding areas.In this paper,the performance of PPP-B2b service is experimentally analyzed first.Then,the ionosphere-free model is established.In order to solve the problem of slow convergence for traditional PPP,an adaptive robust extend Kalman filter(AREKF)algorithm is developed.Unlike the error compensation models,it reflects the noise information in real time by adjusting the covariance matrix of the measurements and the weight matrix of the state vector.The experimental results are analyzed last.Evaluation results indicate that the corrections provided by PPP-B2b can significantly reduce the discontinuous error of the orbits and clock offsets caused by broadcast ephemeris updating.Positioning results confirm that AREKF outperforms EKF both in static and kinematic modes.Around 20%improvement in accuracy and 25%improvement in convergence speed are achieved,making it valuable for PPP processing.展开更多
A two-way adaptive Kalman filter is proposed by combining a two-way filter with an adaptive filter for orbit determination of a maneuvered GEO satellite.A method of using Newton's high-resolution differential form...A two-way adaptive Kalman filter is proposed by combining a two-way filter with an adaptive filter for orbit determination of a maneuvered GEO satellite.A method of using Newton's high-resolution differential formula and polynomial fitting for modeling the thrust force of a maneuvered GEO satellite is developed.The adaptive factor,which balances the contributions of the measurements and the dynamic model information,is determined by using a two-segment function and predicted residual statistics.Simulations with a maneuvered GEO satellite tracked by the Chinese ground tracking network were conducted to verify the performance of the proposed orbit determination technique and the method of thrust force modeling.The results show that refining the thrust force model is beneficial for the orbit determination of a maneuvered GEO satellite;the two-way adaptive Kalman filter can efficiently control the influence of the dynamic model errors on the orbit state estimate.展开更多
Nowadays,flying ad hoc network(FANET)has captured great attention for its huge potential in military and civilian applications.However,the high-speed movement of unmanned aerial vehi-cles(UAVs)in three-dimensional(3D)...Nowadays,flying ad hoc network(FANET)has captured great attention for its huge potential in military and civilian applications.However,the high-speed movement of unmanned aerial vehi-cles(UAVs)in three-dimensional(3D)space leads to fast topology change in FANET and brings new challenges to traditional routing mechanisms.To improve the performance of packet trans-mission in the 3D high dynamic FANETs,we propose a 3D greedy perimeter stateless routing(GPSR)algorithm using adaptive Kalman prediction for FANETs with omnidirectional antenna(KOGPSR).Especially,in data forwarding part of the KOGPSR,we propose a new link metric for greedy forwarding based on a torus-shaped radiation pattern of the omnidirectional antenna of UAVs,and a restricted flooding strategy is introduced to solve the 3D void node problem in geographic routing.In addition,in order to enhance the accuracy of the location information of high dynamic UAVs,we design an adaptive Kalman algorithm to track and predict the motion of UAVs.Finally,a FANET simulation platform based on OPNET is built to depict the performance of the KOGPSR algorithm.The simulation results show that the proposed KOGPSR algorithm is more suitable for the actual 3D high dynamic FANET.展开更多
Inherent flaws in the extended Kalman filter(EKF) algorithm were pointed out and unscented Kalman filter(UKF) was put forward as an alternative.Furthermore,a novel adaptive unscented Kalman filter(AUKF) based on innov...Inherent flaws in the extended Kalman filter(EKF) algorithm were pointed out and unscented Kalman filter(UKF) was put forward as an alternative.Furthermore,a novel adaptive unscented Kalman filter(AUKF) based on innovation was developed.The three data-fusing approaches were analyzed and evaluated in a mathematically rigorous way.Field experiments conducted in lake further demonstrate that AUKF reduces the position error approximately by 65% compared with EKF and by 35% UKF and improves the robust performance.展开更多
To address the issue of insufficient accuracy in attitude estimation using Inertial Measurement Units(IMU),this paper proposes amulti-sensor fusion attitude estimationmethod based on an improved Error-State Kalman Fil...To address the issue of insufficient accuracy in attitude estimation using Inertial Measurement Units(IMU),this paper proposes amulti-sensor fusion attitude estimationmethod based on an improved Error-State Kalman Filter(ESKF).Several adaptive mechanisms are introduced within the standard ESKF framework:first,the process noise covariance is dynamically adjusted based on gyroscope angular velocity to enhance the algorithm’s adaptability under both static and dynamic conditions;second,the Sage-Husa algorithm is employed to estimate the measurement noise covariance of the accelerometer and magnetometer in real-time,mitigating disturbances caused by external accelerations and magnetic fields.Additionally,a dual-mode correction strategy is proposed for yaw angle estimation:a computationally efficient quaternion-based direct correction method is used for small-angle errors,while the system switches to a higher-precision adaptive ESKF algorithm for large-angle deviations.This strategy ensures estimation accuracy while effectively reducing computational complexity.Experimental results in mixed static-dynamic scenarios show that the proposed algorithmachieves the lowest rootmean square error(RMSE)in roll(5.638°)and yaw(6.315°),and ranks first in pitch(2.616°),validating the effectiveness of the improvements.In magnetic interference tests,it delivers the best overall performance,achieving the highest accuracy in roll and yaw and near-optimal performance in pitch,highlighting its excellent anti-interference capability and dynamic tracking performance.Complexity analysis further confirms a significant reduction in computational time compared to the standard ESKF.The results consistently demonstrate that the proposed method offers higher estimation accuracy and robustness under complex conditions,making it suitable for practical applications involving magnetic disturbances and rapid motions.展开更多
To provide stable and accurate position information of control points in a complex coastal environment,an adaptive iterated extended Kalman filter(AIEKF)for fixed-point positioning integrating global navigation satell...To provide stable and accurate position information of control points in a complex coastal environment,an adaptive iterated extended Kalman filter(AIEKF)for fixed-point positioning integrating global navigation satellite system,inertial navigation system,and ultra wide band(UWB)is proposed.In thismethod,the switched global navigation satellite system(GNSS)and UWB measurement are used as the measurement of the proposed filter.For the data fusion filter,the expectation-maximization(EM)based IEKF is used as the forward filter,then,the Rauch-Tung-Striebel smoother for IEKF filter’s result smoothing.Tests illustrate that the proposed AIEKF is able to provide an accurate estimation.展开更多
Vertical tire forces are essential for vehicle modelling and dynamic control.However,an evaluation of the vertical tire forces on a multi-axle truck is difficult to accomplish.The current methods require a large amoun...Vertical tire forces are essential for vehicle modelling and dynamic control.However,an evaluation of the vertical tire forces on a multi-axle truck is difficult to accomplish.The current methods require a large amount of experimental data and many sensors owing to the wide variation of the parameters and the over-constraint.To simplify the design process and reduce the demand of the sensors,this paper presents a practical approach to estimating the vertical tire forces of a multi-axle truck for dynamic control.The estimation system is based on a novel vertical force model and a proposed adaptive treble extend Kalman filter(ATEKF).To adapt to the widely varying parameters,a sliding mode update is designed to make the ATEKF adaptive,and together with the use of an initial setting update and a vertical tire force adjustment,the overall system becomes more robust.In particular,the model aims to eliminate the effects of the over-constraint and the uneven weight distribution.The results show that the ATEKF method achieves an excellent performance in a vertical force evaluation,and its performance is better than that of the treble extend Kalman filter.展开更多
In the normal operation condition, a conventional square-root cubature Kalman filter （SRCKF） gives sufficiently good estimation results. However, if the measurements are not reliable, the SRCKF may give inaccurate r...In the normal operation condition, a conventional square-root cubature Kalman filter （SRCKF） gives sufficiently good estimation results. However, if the measurements are not reliable, the SRCKF may give inaccurate results and diverges by time. This study introduces an adaptive SRCKF algorithm with the filter gain correction for the case of measurement malfunctions. By proposing a switching criterion, an optimal filter is selected from the adaptive and conventional SRCKF according to the measurement quality. A subsystem soft fault detection algorithm is built with the filter residual. Utilizing a clear subsystem fault coefficient, the faulty subsystem is isolated as a result of the system reconstruction. In order to improve the performance of the multi-sensor system, a hybrid fusion algorithm is presented based on the adaptive SRCKF. The state and error covariance matrix are also predicted by the priori fusion estimates, and are updated by the predicted and estimated information of subsystems. The proposed algorithms were applied to the vessel dynamic positioning system simulation. They were compared with normal SRCKF and local estimation weighted fusion algorithm. The simulation results show that the presented adaptive SRCKF improves the robustness of subsystem filtering, and the hybrid fusion algorithm has the better performance. The simulation verifies the effectiveness of the proposed algorithms.展开更多
基金supported in part by the National Key Research and Development Program of China(2023YFB3906403)the National Natural Science Foundation of China(62373118,62173105)the Natural Science Foundation of Heilongjiang Province of China(ZD2023F002)
文摘Over the past few decades, numerous adaptive Kalman filters(AKFs) have been proposed. However, achieving online estimation with both high estimation accuracy and fast convergence speed is challenging, especially when both the process noise and measurement noise covariance matrices are relatively inaccurate. Maximum likelihood estimation(MLE) possesses the potential to achieve this goal, since its theoretical accuracy is guaranteed by asymptotic optimality and the convergence speed is fast due to weak dependence on accurate state estimation.Unfortunately, the maximum likelihood cost function is so intricate that the existing MLE methods can only simply ignore all historical measurement information to achieve online estimation,which cannot adequately realize the potential of MLE. In order to design online MLE-based AKFs with high estimation accuracy and fast convergence speed, an online exploratory MLE approach is proposed, based on which a mini-batch coordinate descent noise covariance matrix estimation framework is developed. In this framework, the maximum likelihood cost function is simplified for online estimation with fewer and simpler terms which are selected in a mini-batch and calculated with a backtracking method. This maximum likelihood cost function is sidestepped and solved by exploring possible estimated noise covariance matrices adaptively while the historical measurement information is adequately utilized. Furthermore, four specific algorithms are derived under this framework to meet different practical requirements in terms of convergence speed, estimation accuracy,and calculation load. Abundant simulations and experiments are carried out to verify the validity and superiority of the proposed algorithms as compared with existing state-of-the-art AKFs.
基金supported by the Foundation of Key Laboratory of Micro-inertial Instrument and Advanced Navigation Technology,Ministry of Education,Chinathe National Natural Science Foundation of China (61873064)
文摘Solar radio burst(SRB)is one of the main natural interference sources of Global Positioning System(GPS)signals and can reduce the signal-to-noise ratio(SNR),directly affecting the tracking performance of GPS receivers.In this paper,a tracking algorithm based on the adaptive Kalman filter(AKF)with carrier-to-noise ratio estimation is proposed and compared with the conventional second-order phase-locked loop tracking algo-rithms and the improved Sage-Husa adaptive Kalman filter(SHAKF)algorithm.It is discovered that when the SRBs occur,the improved SHAKF and the AKF with carrier-to-noise ratio estimation enable stable tracking to loop signals.The conven-tional second-order phase-locked loop tracking algorithms fail to track the receiver signal.The standard deviation of the carrier phase error of the AKF with carrier-to-noise ratio estimation out-performs 50.51%of the improved SHAKF algorithm,showing less fluctuation and better stability.The proposed algorithm is proven to show more excellent adaptability in the severe envi-ronment caused by the SRB occurrence and has better tracking performance.
文摘In order to measure the parameters of flight rocket by using radar,rocket impact point was estimated accurately for rocket trajectory correction.The Kalman filter with adaptive filter gain matrix was adopted.According to the particle trajectory model,the adaptive Kalman filter trajectory model was constructed for removing and filtering the outliers of the parameters during a section of flight detected by three-dimensional data radar and the rocket impact point was extrapolated.The results of numerical simulation show that the outliers and noise in trajectory measurement signal can be removed effectively by using the adaptive Kalman filter and the filter variance can converge in a short period of time.Based on the relation of filtering time and impact point estimation error,choosing the filtering time of 8-10 scan get the minimum estimation error of impact point.
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Project(2013AA06A411)supported by the National High Technology Research and Development Program of ChinaProject(CXZZ14_1374)supported by the Graduate Education Innovation Program of Jiangsu Province,ChinaProject supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions,China
文摘Pure inertial navigation system(INS) has divergent localization errors after a long time. In order to compensate the disadvantage, wireless sensor network(WSN) associated with the INS was applied to estimate the mobile target positioning. Taking traditional Kalman filter(KF) as the framework, the system equation of KF was established by the INS and the observation equation of position errors was built by the WSN. Meanwhile, the observation equation of velocity errors was established by the velocity difference between the INS and WSN, then the covariance matrix of Kalman filter measurement noise was adjusted with fuzzy inference system(FIS), and the fuzzy adaptive Kalman filter(FAKF) based on the INS/WSN was proposed. The simulation results show that the FAKF method has better accuracy and robustness than KF and EKF methods and shows good adaptive capacity with time-varying system noise. Finally, experimental results further prove that FAKF has the fast convergence error, in comparison with KF and EKF methods.
基金National Natural Science Foundation of China(No.61733012)。
文摘As an orientation measurement system,north-finder has been playing a significant role in both military and civilian fields of orientation and control.In this paper,to deal with drawbacks in the conventional north-finding systems,a dynamic strategy based on continuous rotation modulation to measure the rotational angular velocity of the earth is proposed.By modeling the dynamic error,optimizing the process constraint and estimating dynamic noise,a method combining delay compensation and hardware adjustment,and a constrained adaptive Kalman filter(CAKF)algorithm are designed for the north-finding strategy.According to simulation and experiments,the proposed algorithm can achieve the high-precision north-finding with robust and anti-noise performance.
文摘The alignment accuracy of the strap-down inertial navigation system （SINS） of airborne weapon is greatly degraded by the dynamic wing flexure of the aircraft. An adaptive Kalman filter uses innovation sequences based on the maximum likelihood estimated criterion to adapt the system noise covariance matrix and the measurement noise covariance matrix on line, which is used to estimate the misalignment if the model of wing flexure of the aircraft is unknown. From a number of simulations, it is shown that the accuracy of the adaptive Kalman filter is better than the conventional Kalman filter, and the erroneous misalignment models of the wing flexure of aircraft will cause bad estimation results of Kalman filter using attitude match method.
文摘Several filter techniques were available for the GPS position estimation problem of maneuvering vehicle ranging from using different process noises to Interactive Multiple Model (IMM). The limitation of using standard Kalman filters is listed.The performance of proposed adaptive filter is compared with that of the standard ones,two types of dynamic modeling of the maneuvering vehicle are used.The simulation is based on the almanac data of the GPS satellites to compute its feasibility during the simulation time and position on shape 8 track with continuous vehicle maneuvering. The goal is to obtain computationally efficient filter with reasonable accuracy for vehicle in maneuvering situation. The filter proposed is an alternative to the filter proposed in Ref. [1] with low computational burden.
文摘MEMS(micro-electro-mechanical-system)IMU(inertial measurement unit)sensors are characteristically noisy and this presents a serious problem to their effective use.The Kalman filter assumes zero-mean Gaussian process and measurement noise variables,and then recursively computes optimal state estimates.However,establishing the exact noise statistics is a non-trivial task.Additionally,this noise often varies widely in operation.Addressing this challenge is the focus of adaptive Kalman filtering techniques.In the covariance scaling method,the process and measurement noise covariance matrices Q and R are uniformly scaled by a scalar-quantity attenuating window.This study proposes a new approach where individual elements of Q and R are scaled element-wise to ensure more granular adaptation of noise components and hence improve accuracy.In addition,the scaling is performed over a smoothly decreasing window to balance aggressiveness of response and stability in steady state.Experimental results show that the root mean square errors for both pith and roll axes are significantly reduced compared to the conventional noise adaptation method,albeit at a slightly higher computational cost.Specifically,the root mean square pitch errors are 1.1∘under acceleration and 2.1∘under rotation,which are significantly less than the corresponding errors of the adaptive complementary filter and conventional covariance scaling-based adaptive Kalman filter tested under the same conditions.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61004048 and 61201010)
文摘In order to improve the accuracy of the battery state of charge（SOC） estimation, in this paper we take a lithiumion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, the second-order battery system model is introduced. Meanwhile, the temperature and charge rate are introduced into the model. Then, the temperature and the charge rate are adopted to estimate the battery SOC, with the help of the parameters of an adaptive Kalman filter based estimation algorithm model. Afterwards, it is verified by the numerical simulation that in the ideal case, the accuracy of SOC estimation can be enhanced by adding two elements, namely, the temperature and charge rate.Finally, the actual road conditions are simulated with ADVISOR, and the simulation results show that the proposed method improves the accuracy of battery SOC estimation under actual road conditions. Thus, its application scope in engineering is greatly expanded.
基金financially supported by the Russian Science Foundation research project(Grant No.24-11-00191).
文摘The model of partially observed nonlinear system,called extended Kalman filter(EKF),and depending on some unknown parameters is considered.An approximation of the unobserved component is proposed.This approximation is realized in two steps.First a the method of moments estimator of unknown parameter is constructed and then this estimator is substituted in the equations of extended Kalman filter.The obtained equations describe the adaptive extended Kalman filter.The properties of estimator of the unknown parameter and of the unknown state are described in the asymptotic of small noise in observations.
基金Supported by the Foreign Experts Project of the Belt and Road Innovative Talent Exchange(No.DL2023016005L).
文摘This article proposes an adaptive extended Kalman filter(EKF)for nonlinear cyber-physical systems(CPSs)under unknown inputs and non-Gaussian noises.It is known that the traditional extended Kalman filter is applicable to nonlinear systems with Gaussian white noise.The system is reformulated with intermediate variables to expand the application of nonlinear systems under unknown inputs and non-Gaussian noises,which help decompose unknown input estimation into residual tracking and state observation subproblems.By introducing the orthogonal principle of innovation and attenuation factor,the intermediate variables-based filter can improve the estimation performance under non-Gaussian noises and unknown inputs.Simulation results validate the effectiveness of the proposed method.
文摘Beidou-3 navigation satellite system(BDS-3)initiated a real-time service for precise point positioning(PPP)using the B2b signal,mainly for users in China and surrounding areas.In this paper,the performance of PPP-B2b service is experimentally analyzed first.Then,the ionosphere-free model is established.In order to solve the problem of slow convergence for traditional PPP,an adaptive robust extend Kalman filter(AREKF)algorithm is developed.Unlike the error compensation models,it reflects the noise information in real time by adjusting the covariance matrix of the measurements and the weight matrix of the state vector.The experimental results are analyzed last.Evaluation results indicate that the corrections provided by PPP-B2b can significantly reduce the discontinuous error of the orbits and clock offsets caused by broadcast ephemeris updating.Positioning results confirm that AREKF outperforms EKF both in static and kinematic modes.Around 20%improvement in accuracy and 25%improvement in convergence speed are achieved,making it valuable for PPP processing.
基金supported by the Foundation for the Author of National Excellent Doctoral Dissertation of China (Grant No. 2007B51)National Natural Science Foundation of China (Grant Nos. 41174008 and 41020144004)China Postdoctoral Science Foundation (Grant Nos. 20080430148 and200902444)
文摘A two-way adaptive Kalman filter is proposed by combining a two-way filter with an adaptive filter for orbit determination of a maneuvered GEO satellite.A method of using Newton's high-resolution differential formula and polynomial fitting for modeling the thrust force of a maneuvered GEO satellite is developed.The adaptive factor,which balances the contributions of the measurements and the dynamic model information,is determined by using a two-segment function and predicted residual statistics.Simulations with a maneuvered GEO satellite tracked by the Chinese ground tracking network were conducted to verify the performance of the proposed orbit determination technique and the method of thrust force modeling.The results show that refining the thrust force model is beneficial for the orbit determination of a maneuvered GEO satellite;the two-way adaptive Kalman filter can efficiently control the influence of the dynamic model errors on the orbit state estimate.
基金supported in part by the Shaanxi Provincial Key Research and Development Programs(2022ZDLGY05-04,2022ZDLGY05-03,2023-ZDLGY-33,2021ZDLGY04-08)。
文摘Nowadays,flying ad hoc network(FANET)has captured great attention for its huge potential in military and civilian applications.However,the high-speed movement of unmanned aerial vehi-cles(UAVs)in three-dimensional(3D)space leads to fast topology change in FANET and brings new challenges to traditional routing mechanisms.To improve the performance of packet trans-mission in the 3D high dynamic FANETs,we propose a 3D greedy perimeter stateless routing(GPSR)algorithm using adaptive Kalman prediction for FANETs with omnidirectional antenna(KOGPSR).Especially,in data forwarding part of the KOGPSR,we propose a new link metric for greedy forwarding based on a torus-shaped radiation pattern of the omnidirectional antenna of UAVs,and a restricted flooding strategy is introduced to solve the 3D void node problem in geographic routing.In addition,in order to enhance the accuracy of the location information of high dynamic UAVs,we design an adaptive Kalman algorithm to track and predict the motion of UAVs.Finally,a FANET simulation platform based on OPNET is built to depict the performance of the KOGPSR algorithm.The simulation results show that the proposed KOGPSR algorithm is more suitable for the actual 3D high dynamic FANET.
基金Projects(2009AA093302,2002AA401003)supported by the National High-Tech Research and Development Program of ChinaProject(YYYJ-0917)supported by the Knowledge Innovation of Chinese Academy of Sciences+1 种基金Projects(61273334,61233013)supported by the National Natural Science Foundation of ChinaProject(2011010025-401)supported by the Natural Science Foundation of Liaoning Province,China
文摘Inherent flaws in the extended Kalman filter(EKF) algorithm were pointed out and unscented Kalman filter(UKF) was put forward as an alternative.Furthermore,a novel adaptive unscented Kalman filter(AUKF) based on innovation was developed.The three data-fusing approaches were analyzed and evaluated in a mathematically rigorous way.Field experiments conducted in lake further demonstrate that AUKF reduces the position error approximately by 65% compared with EKF and by 35% UKF and improves the robust performance.
文摘To address the issue of insufficient accuracy in attitude estimation using Inertial Measurement Units(IMU),this paper proposes amulti-sensor fusion attitude estimationmethod based on an improved Error-State Kalman Filter(ESKF).Several adaptive mechanisms are introduced within the standard ESKF framework:first,the process noise covariance is dynamically adjusted based on gyroscope angular velocity to enhance the algorithm’s adaptability under both static and dynamic conditions;second,the Sage-Husa algorithm is employed to estimate the measurement noise covariance of the accelerometer and magnetometer in real-time,mitigating disturbances caused by external accelerations and magnetic fields.Additionally,a dual-mode correction strategy is proposed for yaw angle estimation:a computationally efficient quaternion-based direct correction method is used for small-angle errors,while the system switches to a higher-precision adaptive ESKF algorithm for large-angle deviations.This strategy ensures estimation accuracy while effectively reducing computational complexity.Experimental results in mixed static-dynamic scenarios show that the proposed algorithmachieves the lowest rootmean square error(RMSE)in roll(5.638°)and yaw(6.315°),and ranks first in pitch(2.616°),validating the effectiveness of the improvements.In magnetic interference tests,it delivers the best overall performance,achieving the highest accuracy in roll and yaw and near-optimal performance in pitch,highlighting its excellent anti-interference capability and dynamic tracking performance.Complexity analysis further confirms a significant reduction in computational time compared to the standard ESKF.The results consistently demonstrate that the proposed method offers higher estimation accuracy and robustness under complex conditions,making it suitable for practical applications involving magnetic disturbances and rapid motions.
基金supported in part by the Shandong Natural Science Foundation under Grant ZR2020MF067.
文摘To provide stable and accurate position information of control points in a complex coastal environment,an adaptive iterated extended Kalman filter(AIEKF)for fixed-point positioning integrating global navigation satellite system,inertial navigation system,and ultra wide band(UWB)is proposed.In thismethod,the switched global navigation satellite system(GNSS)and UWB measurement are used as the measurement of the proposed filter.For the data fusion filter,the expectation-maximization(EM)based IEKF is used as the forward filter,then,the Rauch-Tung-Striebel smoother for IEKF filter’s result smoothing.Tests illustrate that the proposed AIEKF is able to provide an accurate estimation.
基金Supported by Basic and Applied Basic Research Foundation of Guangdong Province of China(Grant No.2019A1515110763).
文摘Vertical tire forces are essential for vehicle modelling and dynamic control.However,an evaluation of the vertical tire forces on a multi-axle truck is difficult to accomplish.The current methods require a large amount of experimental data and many sensors owing to the wide variation of the parameters and the over-constraint.To simplify the design process and reduce the demand of the sensors,this paper presents a practical approach to estimating the vertical tire forces of a multi-axle truck for dynamic control.The estimation system is based on a novel vertical force model and a proposed adaptive treble extend Kalman filter(ATEKF).To adapt to the widely varying parameters,a sliding mode update is designed to make the ATEKF adaptive,and together with the use of an initial setting update and a vertical tire force adjustment,the overall system becomes more robust.In particular,the model aims to eliminate the effects of the over-constraint and the uneven weight distribution.The results show that the ATEKF method achieves an excellent performance in a vertical force evaluation,and its performance is better than that of the treble extend Kalman filter.
基金Supported by the National Natural Science Foundation of China （50979017, NSFC60775060） the National High Technology Ship Research Project of China （GJCB09001）
文摘In the normal operation condition, a conventional square-root cubature Kalman filter （SRCKF） gives sufficiently good estimation results. However, if the measurements are not reliable, the SRCKF may give inaccurate results and diverges by time. This study introduces an adaptive SRCKF algorithm with the filter gain correction for the case of measurement malfunctions. By proposing a switching criterion, an optimal filter is selected from the adaptive and conventional SRCKF according to the measurement quality. A subsystem soft fault detection algorithm is built with the filter residual. Utilizing a clear subsystem fault coefficient, the faulty subsystem is isolated as a result of the system reconstruction. In order to improve the performance of the multi-sensor system, a hybrid fusion algorithm is presented based on the adaptive SRCKF. The state and error covariance matrix are also predicted by the priori fusion estimates, and are updated by the predicted and estimated information of subsystems. The proposed algorithms were applied to the vessel dynamic positioning system simulation. They were compared with normal SRCKF and local estimation weighted fusion algorithm. The simulation results show that the presented adaptive SRCKF improves the robustness of subsystem filtering, and the hybrid fusion algorithm has the better performance. The simulation verifies the effectiveness of the proposed algorithms.
