A fuzzy adaptive admittance control method based on real-time estimation is proposed for the motion of the hexapod wheeled-legged robot in various environments.Firstly,the mechanical structure of the robot is designed...A fuzzy adaptive admittance control method based on real-time estimation is proposed for the motion of the hexapod wheeled-legged robot in various environments.Firstly,the mechanical structure of the robot is designed,and a control system framework is proposed according to the different motion environments.To address the adaptability issue of the robot foot contact with the ground,a position-based admittance control method is proposed.Secondly,to improve the tracking performance of the robot foot contact force when the ground environment changes,a fuzzy adaptive admittance parameter adjustment method is proposed.Furthermore,to address the problem of sudden changes in the tracking difference of the foot contact force when the ground environment changes,a real-time estimation method is proposed to estimate the dynamic foot contact force.Finally,a simulation experiment is conducted in MATLAB and Simscape to verify the effectiveness of the robot motion control system,admittance control,fuzzy adaptive admittance parameters adjustment,and the realtime estimation method.Through multi-scenario experiments with the robot prototype,the control method demonstrates its effectiveness and adaptability in various environments.展开更多
The development of the adaptive cycle engine is a crucial direction of advanced fighter power sources in the near future.However,this new technology brings more uncertainty to the design of the control system.To addre...The development of the adaptive cycle engine is a crucial direction of advanced fighter power sources in the near future.However,this new technology brings more uncertainty to the design of the control system.To address the versatile thrust demand under complex dynamic characteristics of the adaptive cycle engine,this paper proposes a direct thrust estimation and control method based on the Model-Free Adaptive Control(MFAC)algorithm.First,an improved Sliding Mode Control-MFAC(SMC-MFAC)algorithm has been developed by introducing a sliding mode variable structure into the standard Full Format Dynamic Linearization-MFAC(FFDL-MFAC)and designing self-adaptive weight coefficients.Then a trivariate double-loop direct thrust control structure with a controller-based thrust estimator and an outer command compensation loop has been established.Through thrust feedback and command correction,accurate control under multi-mode and operation conditions is achieved.The main contribution of this paper is the improved algorithm that combines the tracking capability of the MFAC and the robustness of the SMC,thus enhancing the dynamic performance.Considering the requirements of the online thrust feedback,the designed MFAC-based thrust estimator significantly speeds up the calculation.Additionally,the proposed command correction module can achieve the adaptive thrust control without affecting the operation of the inner loop.Simulations and Hardware-in-Loop(HIL)experiments have been performed on an adaptive cycle engine component-level model to investigate the estimation and control effect under different modes and health conditions.The results demonstrate that both the thrust estimation precision and operation speed are significantly improved compared with Extended Kalman Filter(EKF).Furthermore,the system can accelerate the response of the controlled plant,reduce the overshoot,and realize the thrust recovery within the safety range when the engine encounters the degradation.展开更多
Gaze estimation,a crucial non-verbal communication cue,has achieved remarkable progress through convolutional neural networks.However,accurate gaze prediction in uncon-strained environments,particularly in extreme hea...Gaze estimation,a crucial non-verbal communication cue,has achieved remarkable progress through convolutional neural networks.However,accurate gaze prediction in uncon-strained environments,particularly in extreme head poses,partial occlusions,and abnormal lighting,remains challenging.Existing models often struggle to effectively focus on discriminative ocular features,leading to suboptimal performance.To address these limitations,this paper proposes dual-branch gaze estimation with Gaussian mixture distribution heatmaps and dynamic adaptive loss function(DMGDL),a novel dual-branch gaze estimation algorithm.By introducing Gaussian mixture distribution heatmaps centered on pupil positions as spatial attention guides,the model is enabled to prioritize ocular regions.Additionally,a dual-branch network architecture is designed to separately extract features for yaw and pitch angles,enhancing flexibility and mitigating cross-angle interference.A dynamic adaptive loss function is further formulated to address discontinuities in angle estimation,improving robustness and convergence stability.Experimental evaluations on three benchmark datasets demonstrate that DMGDL outperforms state-of-the-art methods,achiev-ing a mean angular error of 3.98°on the Max-Planck institute for informatics face gaze(MPI-IFaceGaze)dataset,10.21°on the physically unconstrained gaze estimation in the wild(Gaze360)dataset and 6.14°on the real-time eye gaze estimation in natural environments(RT-Gene)dataset,exhibiting superior generalization and robustness.展开更多
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
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 new regression algorithm of an adaptive reduced relevance vector machine is proposed to estimate the illumination chromaticity of an image for the purpose of color constancy. Within the framework of sparse Bayesian ...A new regression algorithm of an adaptive reduced relevance vector machine is proposed to estimate the illumination chromaticity of an image for the purpose of color constancy. Within the framework of sparse Bayesian learning, the algorithm extends the relevance vector machine by combining global and local kernels adaptively in the form of multiple kernels, and the improved locality preserving projection （LLP） is then applied to reduce the column dimension of the multiple kernel input matrix to achieve less training time. To estimate the illumination chromaticity, the algorithm is trained by fuzzy central values of chromaticity histograms of a set of images and the corresponding illuminants. Experiments with real images indicate that the proposed algorithm performs better than the support vector machine and the relevance vector machine while requiring less training time than the relevance vector machine.展开更多
Based on the transform-domain characteristics of pilot signals,a band suppression filter is used as a transform-domain filter to restrain the interference of noise in channel estimation.The performance effect on chann...Based on the transform-domain characteristics of pilot signals,a band suppression filter is used as a transform-domain filter to restrain the interference of noise in channel estimation.The performance effect on channel estimation for an orthogonal frequency division multiplex （OFDM） system by different energy coefficients in the transform domain and the energy coefficient under the different signal-to-noise ratios （SNR） are also analyzed.A new energy coefficient expression is deduced.It is theoretically proven that dynamically selecting an energy coefficient can significantly improve the performance of channel estimation.Simulation results show that the proposed algorithm can achieve better performance close to the theoretic bounds of perfect channel estimation. The algorithm is adapted to single-input single-output （SISO） OFDM and multi-input multi-output （MIMO） OFDM systems.展开更多
This paper is concerned with the adaptive robust cubature Kalman filtering problem for the case that the dynamics model error and the measurement model error exist simultaneously in the satellite attitude estimation s...This paper is concerned with the adaptive robust cubature Kalman filtering problem for the case that the dynamics model error and the measurement model error exist simultaneously in the satellite attitude estimation system. By using Hubel-based robust filtering methodology to correct the measurement covariance formulation of cubature Kalman filter, the proposed filtering algorithm could effectively suppress the measurement model error. To further enhance this effect and reduce the impact of the dynamics model error, two different adaptively robust filtering algorithms,one with the optimal adaptive factor based on the estimated covariance matrix of the predicted residuals and the other with multiple fading factors based on strong tracking algorithm, are developed and applied for the satellite attitude estimation. The quaternion is employed to represent the global attitude parameter, and three-dimensional generalized Rodrigues parameters are introduced to define the local attitude error. A multiplicative quaternion error is derived from the local attitude error to maintain quaternion normalization constraint in the filter. Simulation results indicate that the proposed novel algorithm could exhibit higher accuracy and faster convergence compared with the multiplicative extended Kalman filter, the unscented quaternion estimator, and the adaptive robust unscented Kalman filter.展开更多
In this paper, a novel DOA estimation methodology based upon the technology of adaptive nulling antenna is proposed. Initially, the nulling antenna obtains the weight vector by LMS algorithm and power inversion criter...In this paper, a novel DOA estimation methodology based upon the technology of adaptive nulling antenna is proposed. Initially, the nulling antenna obtains the weight vector by LMS algorithm and power inversion criterion.Afterwards, reciprocal of the antenna pattern is defined as the spatial spectrum and the extracted peak values are corresponded to the estimated DOA. Through observation of the spectrum and data analysis of variable steps and SNRs, the simulation results demonstrate that the proposed method can estimate DOA above board. Furthermore, the estimation error of the proposed technique is directly proportional to step size and is inversely proportional to SNR. Unlike the existing MUSIC algorithm, the proposed algorithm has less computational complexity as it eliminates the need of estimating the number of signals and the eigenvalue decomposition of covariance matrix. Also it outperforms MUSIC algorithm, the recently proposed MUSIC-Like algorithm and classical methods by achieving better resolution with narrow width of peaks.展开更多
The current research of master cylinder pressure estimation mainly relies on hydraulic characteristic or vehicle dynamics.But they are not independently applicable to any environment and have their own scope of applic...The current research of master cylinder pressure estimation mainly relies on hydraulic characteristic or vehicle dynamics.But they are not independently applicable to any environment and have their own scope of application.In addition,about the master cylinder pressure control,there are few studies that can simultaneously balance pressure building accuracy,speed,and prevent pressure overshoot and jitter.In this paper,an adaptative fusion method based on electro-hydraulic characteristic and vehicle mode is proposed to estimate the master cylinder pressure.The fusion strategy is mainly based on the prediction performance of two algorithms under different vehicle speeds,pressures,and ABS states.Apart from this,this article also includes real-time prediction of the friction model based on RLS to improve the accuracy of the electro-hydraulic mode.In order to simultaneously balance pressure control accuracy,response speed,and prevent overshoot and jitter,this article proposes an adaptative LQR controller for MC pressure control which uses fuzzy-logic controller to adjust the weights of LQR controller based on target pressure and difference compared with actual pressure.Through mode-in-loop and hardware-in-loop tests in ramp,step and sinusoidal response,the whole estimation and control system is verified based on real hydraulic system and the performance is satisfactory under these scenes.This research proposes an adaptative pressure estimation and control architecture for integrated electro-hydraulic brake system which could eliminate pressure sensors in typical scenarios and ensure the comprehensive performance of pressure control.展开更多
Vision localization methods have been widely used in the motion estimation of unmanned aerial vehicles(UAVs).The noise of the vision location result is usually modeled as a white Gaussian noise so that this location r...Vision localization methods have been widely used in the motion estimation of unmanned aerial vehicles(UAVs).The noise of the vision location result is usually modeled as a white Gaussian noise so that this location result could be utilized as the observation vector in the Kalman filter to estimate the motion of the vehicle.Since the noise of the vision location result is affected by external environment,the variance of the noise is uncertain.However,in previous researches,the variance is usually set as a fixed empirical value,which will lower the accuracy of the motion estimation.The main contribution of this paper is that we proposed a novel adaptive noise variance identification(ANVI) method,which utilizes the special kinematic properties of the UAV for frequency analysis and then adaptively identifies the variance of the noise.The adaptively identified variance is used in the Kalman filter for more accurate motion estimation.The performance of the proposed method is assessed by simulations and field experiments on a quadrotor system.The results illustrate the effectiveness of the method.展开更多
Mobile robots are often subject to multiplicative noise in the target tracking tasks,where the multiplicative measurement noise is correlated with additive measurement noise.In this paper,first,a correlation multiplic...Mobile robots are often subject to multiplicative noise in the target tracking tasks,where the multiplicative measurement noise is correlated with additive measurement noise.In this paper,first,a correlation multiplicative measurement noise model is established.It is able to more accurately represent the measurement error caused by the distance sensor dependence state.Then,the estimated performance mismatch problem of Cubature Kalman Filter(CKF)under multiplicative noise is analyzed.An improved Gaussian filter algorithm is introduced to help obtain the CKF algorithm with correlated multiplicative noise.In practice,the model parameters are unknown or inaccurate,especially the correlation of noise is difficult to obtain,which can lead to a decrease in filtering accuracy or even divergence.To address this,an adaptive CKF algorithm is further provided to achieve reliable state estimation for the unknown noise correlation coefficient and thus the application of the CKF algorithm is extended.Finally,the estimated performance is analyzed theoretically,and the simulation study is conducted to validate the effectiveness of the proposed algorithm.展开更多
The probability hypothesis density(PHD) filter has been recognized as a promising technique for tracking an unknown number of targets. The performance of the PHD filter, however, is sensitive to the available knowledg...The probability hypothesis density(PHD) filter has been recognized as a promising technique for tracking an unknown number of targets. The performance of the PHD filter, however, is sensitive to the available knowledge on model parameters such as the measurement noise variance and those associated with the changes in the maneuvering target trajectories. If these parameters are unknown in advance, the tracking performance may degrade greatly. To address this aspect, this paper proposes to incorporate the adaptive parameter estimation(APE) method in the PHD filter so that the model parameters, which may be static and/or time-varying, can be estimated jointly with target states. The resulting APE-PHD algorithm is implemented using the particle filter(PF), which leads to the PF-APE-PHD filter. Simulations show that the newly proposed algorithm can correctly identify the unknown measurement noise variances, and it is capable of tracking multiple maneuvering targets with abrupt changing parameters in a more robust manner, compared to the multi-model approaches.展开更多
Composition estimation plays very important role in plant operation and control.Extended Kalman filter(EKF) is one of the most common estimators,which has been used in composition estimation of reactive batch distilla...Composition estimation plays very important role in plant operation and control.Extended Kalman filter(EKF) is one of the most common estimators,which has been used in composition estimation of reactive batch distillation,but its performance is heavily dependent on the thermodynamic modeling of vapor-liquid equilibrium,which is difficult to initialize and tune.In this paper an inferential state estimation scheme based on adaptive neuro-fuzzy inference system(ANFIS) ,which is a model base estimator,is employed for composition estimation by using temperature measurements in multicomponent reactive batch distillation.The state estimator is supported by data from a complete dynamic model that includes component and energy balance equations accompanied with thermodynamic relations and reaction kinetics.The mathematical model is verified by pilot plant data.The simulation results show that the ANFIS estimator provides reliable and accurate estimation for component concentrations in reactive batch distillation.The estimated states form a basis for improving the performance of reactive batch distillation either through decision making of an operator or through an automatic closed-loop control scheme.展开更多
Low Resolution Thermal Array Sensors are widely used in several applications in indoor environments. In particular, one of these cheap, small and unobtrusive sensors provides a low-resolution thermal image of the envi...Low Resolution Thermal Array Sensors are widely used in several applications in indoor environments. In particular, one of these cheap, small and unobtrusive sensors provides a low-resolution thermal image of the environment and, unlike cameras;it is capable to detect human heat emission even in dark rooms. The obtained thermal data can be used to monitor older seniors while they are performing daily activities at home, to detect critical situations such as falls. Most of the studies in activity recognition using Thermal Array Sensors require human detection techniques to recognize humans passing in the sensor field of view. This paper aims to improve the accuracy of the algorithms used so far by considering the temperature environment variation. This method leverages an adaptive background estimation and a noise removal technique based on Kalman Filter. In order to properly validate the system, a novel installation of a single sensor has been implemented in a smart environment: the obtained results show an improvement in human detection accuracy with respect to the state of the art, especially in case of disturbed environments.展开更多
For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang For ...For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang-bang evasive maneuver with a random switching time.Combined Fast multiple model adaptive estimation(Fast MMAE)algorithm,the cooperative guidance law takes detection configuration affecting the accuracy of interception into consideration.Introduced the detection error model related to the line-of-sight(LOS)separation angle of two interceptors,an optimal cooperative guidance law solving the optimization problem is designed to modulate the LOS separation angle to reduce the estimation error and improve the interception performance.Due to the uncertainty of the target bang-bang maneuver switching time and the effective fitting of its multi-modal motion,Fast MMAE is introduced to identify its maneuver switching time and estimate the acceleration of the target to track and intercept the target accurately.The designed cooperative optimal guidance law with Fast MMAE has better estimation ability and interception performance than the traditional guidance law and estimation method via Monte Carlo simulation.展开更多
An adaptive channel estimation algorithm for the channel length is proposed to construct a channel estimation model suitable for orthogonal frequency division multiplexing(OFDM)underwater acoustic communication signal...An adaptive channel estimation algorithm for the channel length is proposed to construct a channel estimation model suitable for orthogonal frequency division multiplexing(OFDM)underwater acoustic communication signals for the dependence of traditional channel estimation algorithms on channel length information.This algorithm can be adopted to evaluate channel estimation quality in real time and to adaptively adjust the channel length of the channel estimation algorithm according to the evaluation result,which satisfies the need of accurate estimation of unknown underwater acoustic channels and communication application;based on the study on the relationship between the OFDM communication bit error rate and the subcarrier signal to noise ratio,a self-adjusting optimization scheme for OFDM subcarrier transmitting power is proposed,which realizes underwater communication with the low bit error rate through higher energy efficiency.The validity of the research content is verified through simulation and field experiments.展开更多
Considering that channel estimation plays a crucial role in coherent detection, this paper addresses a method of Recursive-least-squares (RLS) channel estimation with adaptive forgetting factor in wireless space-time ...Considering that channel estimation plays a crucial role in coherent detection, this paper addresses a method of Recursive-least-squares (RLS) channel estimation with adaptive forgetting factor in wireless space-time coded multiple-input and multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Because there are three different forgetting factor scenarios including adaptive, two-step and conventional ones applied to RLS channel estimation, this paper describes the principle of RLS channel estimation and analyzes the impact of different forgetting factor scenarios on the performances of RLS channel estimation. Simulation results proved that the RLS algorithm with adaptive forgetting factor (RLS-A) outperformed that with two-step forgetting factor (RLS-T) or with conventional forgetting factor (RLS-C) in both estimation accuracy and robustness over the multiple-input multiple-output (MIMO) channel, i.e., a wide-sense stationary uncorrelated scattering (WSSUS) and frequency-selective slowly fading channel. Hence, we can employ the RLS-A method by adjusting forgetting factor adaptively to track and estimate channel state parameters successfully in space-time coded MIMO-OFDM systems.展开更多
The paper deals with state estimation problem of nonlinear non-Gaussian discrete dynamic systems for improvement of accuracy and consistency. An efficient new algorithm called the adaptive Gaussian-sum square-root cub...The paper deals with state estimation problem of nonlinear non-Gaussian discrete dynamic systems for improvement of accuracy and consistency. An efficient new algorithm called the adaptive Gaussian-sum square-root cubature Kalman filter（AGSSCKF） with a split-merge scheme is proposed. It is developed based on the squared-root extension of newly introduced cubature Kalman filter（SCKF） and is built within a Gaussian-sum framework. Based on the condition that the probability density functions of process noises and initial state are denoted by a Gaussian sum using optimization method, a bank of SCKF are used as the sub-filters to estimate state of system with the corresponding weights respectively, which is adaptively updated. The new algorithm consists of an adaptive splitting and merging procedure according to a proposed split-decision model based on the nonlinearity degree of measurement. The results of two simulation scenarios（one-dimensional state estimation and bearings-only tracking） show that the proposed filter demonstrates comparable performance to the particle filter with significantly reduced computational cost.展开更多
This paper presents the design,and validation of a new adaptive control system based on quasi-time delay estimation(Q-TDE)augmented with new integral second-order terminal sliding mode control(ISOTSMC)for a manipulato...This paper presents the design,and validation of a new adaptive control system based on quasi-time delay estimation(Q-TDE)augmented with new integral second-order terminal sliding mode control(ISOTSMC)for a manipulator robot with unknown dynamicuncertainty and disturbances.Contrary to the conventional[TDE,the proposed Q-TDE becomes sufficient to invoke a fixed artficial time delay and utilize the past data only of the control input to approximate the unknown system's dynamic uncertainties.The incorporating of new adaptive reachinglaw with ISOTSMCaugmented with Q-TDE policy ensures the continuous performance tracking of the robot manipulator's trajectories using output feedback.This combination may achieve high performance with a significant chattering reducing procedure.By utilizing the Lyapunov function theory,it can be demonstrated that the robot system is stable and all signals in closed-loop are converging in finite time.Consequently,Simulation and comparative studies with two degrees of freedom robot manipulator were carried out to validate the effectiveness of the designed control scheme.展开更多
基金National Natural Science Foundation of China(No.U1831123)。
文摘A fuzzy adaptive admittance control method based on real-time estimation is proposed for the motion of the hexapod wheeled-legged robot in various environments.Firstly,the mechanical structure of the robot is designed,and a control system framework is proposed according to the different motion environments.To address the adaptability issue of the robot foot contact with the ground,a position-based admittance control method is proposed.Secondly,to improve the tracking performance of the robot foot contact force when the ground environment changes,a fuzzy adaptive admittance parameter adjustment method is proposed.Furthermore,to address the problem of sudden changes in the tracking difference of the foot contact force when the ground environment changes,a real-time estimation method is proposed to estimate the dynamic foot contact force.Finally,a simulation experiment is conducted in MATLAB and Simscape to verify the effectiveness of the robot motion control system,admittance control,fuzzy adaptive admittance parameters adjustment,and the realtime estimation method.Through multi-scenario experiments with the robot prototype,the control method demonstrates its effectiveness and adaptability in various environments.
基金supported by National Natural Science Foundation of China(No.52302472)。
文摘The development of the adaptive cycle engine is a crucial direction of advanced fighter power sources in the near future.However,this new technology brings more uncertainty to the design of the control system.To address the versatile thrust demand under complex dynamic characteristics of the adaptive cycle engine,this paper proposes a direct thrust estimation and control method based on the Model-Free Adaptive Control(MFAC)algorithm.First,an improved Sliding Mode Control-MFAC(SMC-MFAC)algorithm has been developed by introducing a sliding mode variable structure into the standard Full Format Dynamic Linearization-MFAC(FFDL-MFAC)and designing self-adaptive weight coefficients.Then a trivariate double-loop direct thrust control structure with a controller-based thrust estimator and an outer command compensation loop has been established.Through thrust feedback and command correction,accurate control under multi-mode and operation conditions is achieved.The main contribution of this paper is the improved algorithm that combines the tracking capability of the MFAC and the robustness of the SMC,thus enhancing the dynamic performance.Considering the requirements of the online thrust feedback,the designed MFAC-based thrust estimator significantly speeds up the calculation.Additionally,the proposed command correction module can achieve the adaptive thrust control without affecting the operation of the inner loop.Simulations and Hardware-in-Loop(HIL)experiments have been performed on an adaptive cycle engine component-level model to investigate the estimation and control effect under different modes and health conditions.The results demonstrate that both the thrust estimation precision and operation speed are significantly improved compared with Extended Kalman Filter(EKF).Furthermore,the system can accelerate the response of the controlled plant,reduce the overshoot,and realize the thrust recovery within the safety range when the engine encounters the degradation.
基金supported by the Key Project of the NationalLanguage Commission(No.ZDI145-110)the AcademicResearch Projects of Beijing Union University(No.ZK20202514)+1 种基金the Key Laboratory Project(No.YYZN-2024-6)the Project for the Construction and Support of High-Level Innovative Teams in Beijing Municipal Institutions(No.BPHR20220121).
文摘Gaze estimation,a crucial non-verbal communication cue,has achieved remarkable progress through convolutional neural networks.However,accurate gaze prediction in uncon-strained environments,particularly in extreme head poses,partial occlusions,and abnormal lighting,remains challenging.Existing models often struggle to effectively focus on discriminative ocular features,leading to suboptimal performance.To address these limitations,this paper proposes dual-branch gaze estimation with Gaussian mixture distribution heatmaps and dynamic adaptive loss function(DMGDL),a novel dual-branch gaze estimation algorithm.By introducing Gaussian mixture distribution heatmaps centered on pupil positions as spatial attention guides,the model is enabled to prioritize ocular regions.Additionally,a dual-branch network architecture is designed to separately extract features for yaw and pitch angles,enhancing flexibility and mitigating cross-angle interference.A dynamic adaptive loss function is further formulated to address discontinuities in angle estimation,improving robustness and convergence stability.Experimental evaluations on three benchmark datasets demonstrate that DMGDL outperforms state-of-the-art methods,achiev-ing a mean angular error of 3.98°on the Max-Planck institute for informatics face gaze(MPI-IFaceGaze)dataset,10.21°on the physically unconstrained gaze estimation in the wild(Gaze360)dataset and 6.14°on the real-time eye gaze estimation in natural environments(RT-Gene)dataset,exhibiting superior generalization and robustness.
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.
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The National Natural Science Foundation of China(No60573139)the Innovation Foundation of Xidian University forGraduates (No05008)
文摘A new regression algorithm of an adaptive reduced relevance vector machine is proposed to estimate the illumination chromaticity of an image for the purpose of color constancy. Within the framework of sparse Bayesian learning, the algorithm extends the relevance vector machine by combining global and local kernels adaptively in the form of multiple kernels, and the improved locality preserving projection （LLP） is then applied to reduce the column dimension of the multiple kernel input matrix to achieve less training time. To estimate the illumination chromaticity, the algorithm is trained by fuzzy central values of chromaticity histograms of a set of images and the corresponding illuminants. Experiments with real images indicate that the proposed algorithm performs better than the support vector machine and the relevance vector machine while requiring less training time than the relevance vector machine.
文摘Based on the transform-domain characteristics of pilot signals,a band suppression filter is used as a transform-domain filter to restrain the interference of noise in channel estimation.The performance effect on channel estimation for an orthogonal frequency division multiplex （OFDM） system by different energy coefficients in the transform domain and the energy coefficient under the different signal-to-noise ratios （SNR） are also analyzed.A new energy coefficient expression is deduced.It is theoretically proven that dynamically selecting an energy coefficient can significantly improve the performance of channel estimation.Simulation results show that the proposed algorithm can achieve better performance close to the theoretic bounds of perfect channel estimation. The algorithm is adapted to single-input single-output （SISO） OFDM and multi-input multi-output （MIMO） OFDM systems.
基金co-supported by the National Natural Science Foundation of China (No. 61573113)the Harbin Research Foundation for Leaders of Outstanding Disciplines, China (No. 2014RFXXJ074)
文摘This paper is concerned with the adaptive robust cubature Kalman filtering problem for the case that the dynamics model error and the measurement model error exist simultaneously in the satellite attitude estimation system. By using Hubel-based robust filtering methodology to correct the measurement covariance formulation of cubature Kalman filter, the proposed filtering algorithm could effectively suppress the measurement model error. To further enhance this effect and reduce the impact of the dynamics model error, two different adaptively robust filtering algorithms,one with the optimal adaptive factor based on the estimated covariance matrix of the predicted residuals and the other with multiple fading factors based on strong tracking algorithm, are developed and applied for the satellite attitude estimation. The quaternion is employed to represent the global attitude parameter, and three-dimensional generalized Rodrigues parameters are introduced to define the local attitude error. A multiplicative quaternion error is derived from the local attitude error to maintain quaternion normalization constraint in the filter. Simulation results indicate that the proposed novel algorithm could exhibit higher accuracy and faster convergence compared with the multiplicative extended Kalman filter, the unscented quaternion estimator, and the adaptive robust unscented Kalman filter.
基金support of the Science and Technology Commission of Chongqing through the Nature Science Fund (2013jj B40005)supported by the Fundamental Research Funds for the Central University (106112016CDJZR165508) of China
文摘In this paper, a novel DOA estimation methodology based upon the technology of adaptive nulling antenna is proposed. Initially, the nulling antenna obtains the weight vector by LMS algorithm and power inversion criterion.Afterwards, reciprocal of the antenna pattern is defined as the spatial spectrum and the extracted peak values are corresponded to the estimated DOA. Through observation of the spectrum and data analysis of variable steps and SNRs, the simulation results demonstrate that the proposed method can estimate DOA above board. Furthermore, the estimation error of the proposed technique is directly proportional to step size and is inversely proportional to SNR. Unlike the existing MUSIC algorithm, the proposed algorithm has less computational complexity as it eliminates the need of estimating the number of signals and the eigenvalue decomposition of covariance matrix. Also it outperforms MUSIC algorithm, the recently proposed MUSIC-Like algorithm and classical methods by achieving better resolution with narrow width of peaks.
基金Supported by National Natural Science Foundation of China(Grant Nos.52202494,52202495)Chongqing Special Project for Technological Innovation and Application Development(Grant No.CSTB2022TIAD-DEX0014).
文摘The current research of master cylinder pressure estimation mainly relies on hydraulic characteristic or vehicle dynamics.But they are not independently applicable to any environment and have their own scope of application.In addition,about the master cylinder pressure control,there are few studies that can simultaneously balance pressure building accuracy,speed,and prevent pressure overshoot and jitter.In this paper,an adaptative fusion method based on electro-hydraulic characteristic and vehicle mode is proposed to estimate the master cylinder pressure.The fusion strategy is mainly based on the prediction performance of two algorithms under different vehicle speeds,pressures,and ABS states.Apart from this,this article also includes real-time prediction of the friction model based on RLS to improve the accuracy of the electro-hydraulic mode.In order to simultaneously balance pressure control accuracy,response speed,and prevent overshoot and jitter,this article proposes an adaptative LQR controller for MC pressure control which uses fuzzy-logic controller to adjust the weights of LQR controller based on target pressure and difference compared with actual pressure.Through mode-in-loop and hardware-in-loop tests in ramp,step and sinusoidal response,the whole estimation and control system is verified based on real hydraulic system and the performance is satisfactory under these scenes.This research proposes an adaptative pressure estimation and control architecture for integrated electro-hydraulic brake system which could eliminate pressure sensors in typical scenarios and ensure the comprehensive performance of pressure control.
基金supported by National Science and Technology Major Projects of the Ministry of Science and Technology of China:ITER(No.2012GB102007)
文摘Vision localization methods have been widely used in the motion estimation of unmanned aerial vehicles(UAVs).The noise of the vision location result is usually modeled as a white Gaussian noise so that this location result could be utilized as the observation vector in the Kalman filter to estimate the motion of the vehicle.Since the noise of the vision location result is affected by external environment,the variance of the noise is uncertain.However,in previous researches,the variance is usually set as a fixed empirical value,which will lower the accuracy of the motion estimation.The main contribution of this paper is that we proposed a novel adaptive noise variance identification(ANVI) method,which utilizes the special kinematic properties of the UAV for frequency analysis and then adaptively identifies the variance of the noise.The adaptively identified variance is used in the Kalman filter for more accurate motion estimation.The performance of the proposed method is assessed by simulations and field experiments on a quadrotor system.The results illustrate the effectiveness of the method.
基金supported by the National Natural Science Foundation of China(Nos.61773147 and 62033010)Zhejiang Provincial Nature Science Foundation of China(Nos.LR17F030005 and LZ21F030004)Key-Area Research and Development Program of Guangdong Province,china(No.2018B010107002)。
文摘Mobile robots are often subject to multiplicative noise in the target tracking tasks,where the multiplicative measurement noise is correlated with additive measurement noise.In this paper,first,a correlation multiplicative measurement noise model is established.It is able to more accurately represent the measurement error caused by the distance sensor dependence state.Then,the estimated performance mismatch problem of Cubature Kalman Filter(CKF)under multiplicative noise is analyzed.An improved Gaussian filter algorithm is introduced to help obtain the CKF algorithm with correlated multiplicative noise.In practice,the model parameters are unknown or inaccurate,especially the correlation of noise is difficult to obtain,which can lead to a decrease in filtering accuracy or even divergence.To address this,an adaptive CKF algorithm is further provided to achieve reliable state estimation for the unknown noise correlation coefficient and thus the application of the CKF algorithm is extended.Finally,the estimated performance is analyzed theoretically,and the simulation study is conducted to validate the effectiveness of the proposed algorithm.
基金supported by the National Natural Science Foundation of China (Nos. 61305017, 61304264)the Natural Science Foundation of Jiangsu Province (No. BK20130154)
文摘The probability hypothesis density(PHD) filter has been recognized as a promising technique for tracking an unknown number of targets. The performance of the PHD filter, however, is sensitive to the available knowledge on model parameters such as the measurement noise variance and those associated with the changes in the maneuvering target trajectories. If these parameters are unknown in advance, the tracking performance may degrade greatly. To address this aspect, this paper proposes to incorporate the adaptive parameter estimation(APE) method in the PHD filter so that the model parameters, which may be static and/or time-varying, can be estimated jointly with target states. The resulting APE-PHD algorithm is implemented using the particle filter(PF), which leads to the PF-APE-PHD filter. Simulations show that the newly proposed algorithm can correctly identify the unknown measurement noise variances, and it is capable of tracking multiple maneuvering targets with abrupt changing parameters in a more robust manner, compared to the multi-model approaches.
文摘Composition estimation plays very important role in plant operation and control.Extended Kalman filter(EKF) is one of the most common estimators,which has been used in composition estimation of reactive batch distillation,but its performance is heavily dependent on the thermodynamic modeling of vapor-liquid equilibrium,which is difficult to initialize and tune.In this paper an inferential state estimation scheme based on adaptive neuro-fuzzy inference system(ANFIS) ,which is a model base estimator,is employed for composition estimation by using temperature measurements in multicomponent reactive batch distillation.The state estimator is supported by data from a complete dynamic model that includes component and energy balance equations accompanied with thermodynamic relations and reaction kinetics.The mathematical model is verified by pilot plant data.The simulation results show that the ANFIS estimator provides reliable and accurate estimation for component concentrations in reactive batch distillation.The estimated states form a basis for improving the performance of reactive batch distillation either through decision making of an operator or through an automatic closed-loop control scheme.
文摘Low Resolution Thermal Array Sensors are widely used in several applications in indoor environments. In particular, one of these cheap, small and unobtrusive sensors provides a low-resolution thermal image of the environment and, unlike cameras;it is capable to detect human heat emission even in dark rooms. The obtained thermal data can be used to monitor older seniors while they are performing daily activities at home, to detect critical situations such as falls. Most of the studies in activity recognition using Thermal Array Sensors require human detection techniques to recognize humans passing in the sensor field of view. This paper aims to improve the accuracy of the algorithms used so far by considering the temperature environment variation. This method leverages an adaptive background estimation and a noise removal technique based on Kalman Filter. In order to properly validate the system, a novel installation of a single sensor has been implemented in a smart environment: the obtained results show an improvement in human detection accuracy with respect to the state of the art, especially in case of disturbed environments.
基金This work was supported by the National Natural Science Foundation(NNSF)of China under grant no.61673386,62073335the China Postdoctoral Science Foundation(2017M613201,2019T120944).
文摘For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang-bang evasive maneuver with a random switching time.Combined Fast multiple model adaptive estimation(Fast MMAE)algorithm,the cooperative guidance law takes detection configuration affecting the accuracy of interception into consideration.Introduced the detection error model related to the line-of-sight(LOS)separation angle of two interceptors,an optimal cooperative guidance law solving the optimization problem is designed to modulate the LOS separation angle to reduce the estimation error and improve the interception performance.Due to the uncertainty of the target bang-bang maneuver switching time and the effective fitting of its multi-modal motion,Fast MMAE is introduced to identify its maneuver switching time and estimate the acceleration of the target to track and intercept the target accurately.The designed cooperative optimal guidance law with Fast MMAE has better estimation ability and interception performance than the traditional guidance law and estimation method via Monte Carlo simulation.
基金supported by the National Natural Science Foundation of China(51679247)
文摘An adaptive channel estimation algorithm for the channel length is proposed to construct a channel estimation model suitable for orthogonal frequency division multiplexing(OFDM)underwater acoustic communication signals for the dependence of traditional channel estimation algorithms on channel length information.This algorithm can be adopted to evaluate channel estimation quality in real time and to adaptively adjust the channel length of the channel estimation algorithm according to the evaluation result,which satisfies the need of accurate estimation of unknown underwater acoustic channels and communication application;based on the study on the relationship between the OFDM communication bit error rate and the subcarrier signal to noise ratio,a self-adjusting optimization scheme for OFDM subcarrier transmitting power is proposed,which realizes underwater communication with the low bit error rate through higher energy efficiency.The validity of the research content is verified through simulation and field experiments.
基金Project supported by the National Natural Science Foundation of China (No. 60272079), and the Hi-Tech Research and Development Program (863) of China (No. 2003AA123310)
文摘Considering that channel estimation plays a crucial role in coherent detection, this paper addresses a method of Recursive-least-squares (RLS) channel estimation with adaptive forgetting factor in wireless space-time coded multiple-input and multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Because there are three different forgetting factor scenarios including adaptive, two-step and conventional ones applied to RLS channel estimation, this paper describes the principle of RLS channel estimation and analyzes the impact of different forgetting factor scenarios on the performances of RLS channel estimation. Simulation results proved that the RLS algorithm with adaptive forgetting factor (RLS-A) outperformed that with two-step forgetting factor (RLS-T) or with conventional forgetting factor (RLS-C) in both estimation accuracy and robustness over the multiple-input multiple-output (MIMO) channel, i.e., a wide-sense stationary uncorrelated scattering (WSSUS) and frequency-selective slowly fading channel. Hence, we can employ the RLS-A method by adjusting forgetting factor adaptively to track and estimate channel state parameters successfully in space-time coded MIMO-OFDM systems.
基金supported by the National Natural Science Foundation of China(No. 61032001)Shandong Provincial Natural Science Foundation of China (No. ZR2012FQ004)
文摘The paper deals with state estimation problem of nonlinear non-Gaussian discrete dynamic systems for improvement of accuracy and consistency. An efficient new algorithm called the adaptive Gaussian-sum square-root cubature Kalman filter（AGSSCKF） with a split-merge scheme is proposed. It is developed based on the squared-root extension of newly introduced cubature Kalman filter（SCKF） and is built within a Gaussian-sum framework. Based on the condition that the probability density functions of process noises and initial state are denoted by a Gaussian sum using optimization method, a bank of SCKF are used as the sub-filters to estimate state of system with the corresponding weights respectively, which is adaptively updated. The new algorithm consists of an adaptive splitting and merging procedure according to a proposed split-decision model based on the nonlinearity degree of measurement. The results of two simulation scenarios（one-dimensional state estimation and bearings-only tracking） show that the proposed filter demonstrates comparable performance to the particle filter with significantly reduced computational cost.
文摘This paper presents the design,and validation of a new adaptive control system based on quasi-time delay estimation(Q-TDE)augmented with new integral second-order terminal sliding mode control(ISOTSMC)for a manipulator robot with unknown dynamicuncertainty and disturbances.Contrary to the conventional[TDE,the proposed Q-TDE becomes sufficient to invoke a fixed artficial time delay and utilize the past data only of the control input to approximate the unknown system's dynamic uncertainties.The incorporating of new adaptive reachinglaw with ISOTSMCaugmented with Q-TDE policy ensures the continuous performance tracking of the robot manipulator's trajectories using output feedback.This combination may achieve high performance with a significant chattering reducing procedure.By utilizing the Lyapunov function theory,it can be demonstrated that the robot system is stable and all signals in closed-loop are converging in finite time.Consequently,Simulation and comparative studies with two degrees of freedom robot manipulator were carried out to validate the effectiveness of the designed control scheme.
