In real-world applications, datasets frequently contain outliers, which can hinder the generalization ability of machine learning models. Bayesian classifiers, a popular supervised learning method, rely on accurate pr...In real-world applications, datasets frequently contain outliers, which can hinder the generalization ability of machine learning models. Bayesian classifiers, a popular supervised learning method, rely on accurate probability density estimation for classifying continuous datasets. However, achieving precise density estimation with datasets containing outliers poses a significant challenge. This paper introduces a Bayesian classifier that utilizes optimized robust kernel density estimation to address this issue. Our proposed method enhances the accuracy of probability density distribution estimation by mitigating the impact of outliers on the training sample’s estimated distribution. Unlike the conventional kernel density estimator, our robust estimator can be seen as a weighted kernel mapping summary for each sample. This kernel mapping performs the inner product in the Hilbert space, allowing the kernel density estimation to be considered the average of the samples’ mapping in the Hilbert space using a reproducing kernel. M-estimation techniques are used to obtain accurate mean values and solve the weights. Meanwhile, complete cross-validation is used as the objective function to search for the optimal bandwidth, which impacts the estimator. The Harris Hawks Optimisation optimizes the objective function to improve the estimation accuracy. The experimental results show that it outperforms other optimization algorithms regarding convergence speed and objective function value during the bandwidth search. The optimal robust kernel density estimator achieves better fitness performance than the traditional kernel density estimator when the training data contains outliers. The Naïve Bayesian with optimal robust kernel density estimation improves the generalization in the classification with outliers.展开更多
In practical multi-sensor information fusion systems, there exists uncertainty about the network structure, active state of sensors, and information itself （including fuzziness, randomness, incompleteness as well as ...In practical multi-sensor information fusion systems, there exists uncertainty about the network structure, active state of sensors, and information itself （including fuzziness, randomness, incompleteness as well as roughness, etc）. Hence it requires investigating the problem of uncertain information fusion. Robust learning algorithm which adapts to complex environment and the fuzzy inference algorithm which disposes fuzzy information are explored to solve the problem. Based on the fusion technology of neural networks and fuzzy inference algorithm, a multi-sensor uncertain information fusion system is modeled. Also RANFIS learning algorithm and fusing weight synthesized inference algorithm are developed from the ANFIS algorithm according to the concept of robust neural networks. This fusion system mainly consists of RANFIS confidence estimator, fusing weight synthesized inference knowledge base and weighted fusion section. The simulation result demonstrates that the proposed fusion model and algorithm have the capability of uncertain information fusion, thus is obviously advantageous compared with the conventional Kalman weighted fusion algorithm.展开更多
The parameter estimation problem in linear model is considered when multicollinearity and outliers exist simultaneously.A class of new estimators,robust general shrunken estimators,are proposed by grafting the robust ...The parameter estimation problem in linear model is considered when multicollinearity and outliers exist simultaneously.A class of new estimators,robust general shrunken estimators,are proposed by grafting the robust estimation techniques philosophy into the biased estimator,and their statistical properties are discussed.By appropriate choices of the shrinking parameter matrix,we obtain many useful and important estimators.A numerical example is used to illustrate that these new estimators can not only effectively overcome difficulty caused by multicollinearity but also resist the influence of outliers.展开更多
The recovery of coseismic 3-dimensional(3D)surface deformation field plays a crucial role in studying seismic source characteristics and earthquake hazards.As of now,there are two main types of methods for recovering ...The recovery of coseismic 3-dimensional(3D)surface deformation field plays a crucial role in studying seismic source characteristics and earthquake hazards.As of now,there are two main types of methods for recovering coseismic 3D surface deformations:the first type is to solve the problem directly by using the least squares method based on observations from three or more viewpoints,and the second type solves the problem by combining ascending and descending In SAR line-of-sight(LOS)observations with constraint models.The former type is mainly applicable to surface rupture earthquakes,because when an earthquake ruptures to the surface,we can usually obtain surface deformation observations from three or more views.The latter type applies to earthquakes that do not cause surface ruptures and have extensive blind faults.Currently,most research focuses on improving the above types of methods.However,some key factors in the coseismic 3D surface deformation inversion are rarely mentioned,such as the influence of window size on the inversion results in the strain model and variance component estimation method(SM-VCE),and whether the outliers in the observational data are considered.So,we developed a new chain of integrating In SAR observation and SM-VCE model to systematically assess the impacts of window size and outliers on coseismic 3D surface deformation inversions.Through simulation experiments,we observed that the selection of window size significantly impacts the accuracy of the results.Specifically,larger window sizes lead to wider residuals ranges in the fault region,resulting in the loss of extreme solution values when using a window of 15×15 pixels(i.e.,7.5 km×7.5 km).Hence,we recommend utilizing windows with 7×7 pixels or 9×9 pixels for optimal accuracy,as larger window sizes diminish the significance of the outcomes.The elimination of points displaying different deformation directions helps reduce residuals and preserves near-field deformation results.Furthermore,the residuals along the 3D direction can be reduced by 10%-30%when a small set of points is selected using a method based on Euclidean distances.However,when 441 points were selected,the vertical residuals increased by 22%compared to 81 points.Integration of the SM-VCE algorithm with robust estimation techniques effectively minimizes far-field deformation errors in data,thereby marginally enhancing the near-field deformation solution.展开更多
This study investigated the problems of non-cooperative target recognition and relative motion estimation during spacecraft rendezvous maneuvers.A structure integrating an Inertial Measurement Unit(IMU)and a visual ca...This study investigated the problems of non-cooperative target recognition and relative motion estimation during spacecraft rendezvous maneuvers.A structure integrating an Inertial Measurement Unit(IMU)and a visual camera was presented.The angular velocity output of the IMU was used to calculate the motion trajectories of star points in multiple image frames,which can highlight the motion of non-cooperative targets with respect to the image background to improve the probability of target recognition.To solve the problem of target misidentification caused by new star points entering the field of view,a target-tracking link based on IMU prediction was introduced to track the position of the target in the image.Furthermore,a measurement model was constructed using the line-of-sight vector generated from target recognition,and the relative motion state was estimated using a Huber-based non-linear filter.Semi-physical and numerical simulations were performed to evaluate the effectiveness and efficiency of the proposed method.展开更多
In micro-electro-mechanical system based inertial navigation system（MEMS-INS）/global position system（GPS） integrated navigation systems, there exist unknown disturbances and abnormal measurements. In order to obta...In micro-electro-mechanical system based inertial navigation system（MEMS-INS）/global position system（GPS） integrated navigation systems, there exist unknown disturbances and abnormal measurements. In order to obtain high estimation accuracy and enhance detection sensitivity to faults in measurements, this paper deals with the problem of model-based robust estimation（RE） and fault detection（FD）. A filter gain matrix and a post-filter are designed to obtain a RE and FD algorithm with current measurements, which is different from most of the existing priori filters using measurements in one-step delay. With the designed filter gain matrix, the H-infinity norm of the transfer function from noise inputs to estimation error outputs is limited within a certain range; with the designed post-filter, the residual signal is robust to disturbances but sensitive to faults. Therefore, the algorithm can guarantee small estimation errors in the presence of disturbances and have high sensitivity to faults. The proposed method is evaluated in an integrated navigation system, and the simulation results show that it is more effective in position estimation and fault signal detection than priori RE and FD algorithms.展开更多
Depending on analyzing the abuse of equivalent weights,a set of self-contained theory system on robust estimation based on equivalent variance-covariance is established,which includes ρ function, φ function,equivale...Depending on analyzing the abuse of equivalent weights,a set of self-contained theory system on robust estimation based on equivalent variance-covariance is established,which includes ρ function, φ function,equivalent variance-covariance function,influence function and breakdown point.And an example is given to verify that the robust models proposed in this paper are reliable and correct.展开更多
Once the spoofer has controlled the navigation sys-tem of unmanned aerial vehicle(UAV),it is hard to effectively control the error convergence to meet the threshold condition only by adjusting parameters of estimation...Once the spoofer has controlled the navigation sys-tem of unmanned aerial vehicle(UAV),it is hard to effectively control the error convergence to meet the threshold condition only by adjusting parameters of estimation if estimation of the spoofer on UAV has continuous observation error.Aiming at this problem,the influence of the spoofer’s state estimation error on spoofing effect and error convergence conditions is theoretically analyzed,and an improved adaptively robust estimation algo-rithm suitable for steady-state linear quadratic estimator is pro-posed.It enables the spoofer’s estimator to reliably estimate UAV status in real time,improves the robustness of the estima-tor in responding to observation errors,and accelerates the con-vergence time of error control.Simulation experiments show that the mean value of normalized innovation squared(NIS)is reduced by 88.5%,and the convergence time of NIS value is reduced by 76.3%,the convergence time of true trajectory error of UAV is reduced by 42.3%,the convergence time of estimated trajectory error of UAV is reduced by 67.4%,the convergence time of estimated trajectory error of the spoofer is reduced by 33.7%,and the convergence time of broadcast trajectory error of the spoofer is reduced by 54.8%when the improved algorithm is used.The improved algorithm can make UAV deviate from pre-set trajectory to spoofing trajectory more effectively and more subtly.展开更多
Data reconciliation is an effective technique for providing accurate and consistent value for chemical process. However, the presence of gross errors can severely bias the reconciled results. Robust estimators can sig...Data reconciliation is an effective technique for providing accurate and consistent value for chemical process. However, the presence of gross errors can severely bias the reconciled results. Robust estimators can significantly reduce the effect of gross errors and yield less-biased results. In this article, a new method is proposed to solve the robust data reconciliation problem of nonlinear chemical process. By using several technologies including linearization method, penalty function, virtual observation equation, and equivalent weights method, the robust data reconciliation problem can be transformed into least squares estimator problem which leads to the convenience in computation. Simulation results in a nonlinear chemical process demonstrate the efficiency of the proposed method.展开更多
Full duplex radio increases the frequency efficiency but its performance is limited by the self-interference （SI）. We first analyze the multiple noises in the full duplex radio system and model such noises as an α ...Full duplex radio increases the frequency efficiency but its performance is limited by the self-interference （SI）. We first analyze the multiple noises in the full duplex radio system and model such noises as an α - stable distribution. Then we formulate a novel non-Gaussian SI problem. Under the maximum correntropy criterion （MCC）, a robust digital non-linear self-interference cancellation algorithm is proposed for the SI channel estimation. A gradient descent based algorithm is derived to search the optimal solution. Simulation results show that the proposed algorithm can achieve a smaller estimation error and a higher pseudo signal to interference plus noise ratio （PSINR） than the well-known least mean square （LMS） algorithm and least square （LS） algorithm.展开更多
This paper proposes a new robust video stabilization algorithm to remove unwanted vibrations in video sequences. A complete theoretical analysis is first established for video stabilization, providing a basis for new ...This paper proposes a new robust video stabilization algorithm to remove unwanted vibrations in video sequences. A complete theoretical analysis is first established for video stabilization, providing a basis for new stabilization algorithm. Secondly, a new robust global motion estimation (GME) algorithm is proposed. Different from classic methods, the GME algorithm is based on spatial-temporal filtered motion vectors computed by block-matching methods. In addition, effective schemes are employed in correction phase to prevent boundary artifacts and error accumulation. Experiments show that the proposed algorithm has satisfactory stabilization effects while maintaining good tradeoff between speed and precision.展开更多
Integrity is significant for safety-of-life applications. Receiver autonomous integrity monitoring（RAIM） has been developed to provide integrity service for civil aviation. At first,the conventional RAIM algorithm i...Integrity is significant for safety-of-life applications. Receiver autonomous integrity monitoring（RAIM） has been developed to provide integrity service for civil aviation. At first,the conventional RAIM algorithm is only suitable for single fault detection, single GNSS constellation. However, multiple satellite failure should be considered when more than one satellite navigation system are adopted. To detect and exclude multi-fault, most current algorithms perform an iteration procedure considering all possible fault model which lead to heavy computation burden. An alternative RAIM is presented in this paper based on multiple satellite constellations（for example, GPS and Bei Dou（BDS） etc.） and robust estimation for multi-fault detection and exclusion, which can not only detect multi-failures,but also control the influences of near failure observation. Besides, the RAIM algorithm based on robust estimation is more efficient than the current RAIM algorithm for multiple constellation and multiple faults. Finally, the algorithm is tested by GPS/Bei Dou data.展开更多
A robust low-carbon economic optimal scheduling method that considers source-load uncertainty and hydrogen energy utilization is developed.The proposed method overcomes the challenge of source-load random fluctuations...A robust low-carbon economic optimal scheduling method that considers source-load uncertainty and hydrogen energy utilization is developed.The proposed method overcomes the challenge of source-load random fluctuations in integrated energy systems(IESs)in the operation scheduling problem of integrated energy production units(IEPUs).First,to solve the problem of inaccurate prediction of renewable energy output,an improved robust kernel density estimation method is proposed to construct a data-driven uncertainty output set of renewable energy sources statistically and build a typical scenario of load uncertainty using stochastic scenario reduction.Subsequently,to resolve the problem of insufficient utilization of hydrogen energy in existing IEPUs,a robust low-carbon economic optimal scheduling model of the source-load interaction of an IES with a hydrogen energy system is established.The system considers the further utilization of energy using hydrogen energy coupling equipment(such as hydrogen storage devices and fuel cells)and the comprehensive demand response of load-side schedulable resources.The simulation results show that the proposed robust stochastic optimization model driven by data can effectively reduce carbon dioxide emissions,improve the source-load interaction of the IES,realize the efficient use of hydrogen energy,and improve system robustness.展开更多
In this paper, we investigate state estimations of a dynamical system in which not only process and measurement noise, but also parameter uncertainties and deterministic input signals are involved. The sensitivity pen...In this paper, we investigate state estimations of a dynamical system in which not only process and measurement noise, but also parameter uncertainties and deterministic input signals are involved. The sensitivity penalization based robust state estimation is extended to uncertain linear systems with deterministic input signals and parametric uncertainties which may nonlinearly affect a state-space plant model. The form of the derived robust estimator is similar to that of the well-known Kalman filter with a comparable computational complexity. Under a few weak assumptions, it is proved that though the derived state estimator is biased, the bound of estimation errors is finite and the covariance matrix of estimation errors is bounded. Numerical simulations show that the obtained robust filter has relatively nice estimation performances.展开更多
Based on Bayesian theory and RANSAC,this paper applies Bayesian Sampling Consensus(BaySAC)method using convergence evaluation of hypothesis models in indoor point cloud processing.We implement a conditional sampling m...Based on Bayesian theory and RANSAC,this paper applies Bayesian Sampling Consensus(BaySAC)method using convergence evaluation of hypothesis models in indoor point cloud processing.We implement a conditional sampling method,BaySAC,to always select the minimum number of required data with the highest inlier probabilities.Because the primitive parameters calculated by the different inlier sets should be convergent,this paper presents a statistical testing algorithm for a candidate model parameter histogram to compute the prior probability of each data point.Moreover,the probability update is implemented using the simplified Bayes’formula.The performances of the BaySAC algorithm with the proposed strategies of the prior probability determination and the RANSAC framework are compared using real data-sets.The experimental results indicate that the more outliers contain the data points,the higher computational efficiency of our proposed algorithm gains compared with RANSAC.The results also indicate that the proposed statistical testing strategy can determine sound prior inlier probability free of the change of hypothesis models.展开更多
The conventional prediction of milling stability has been extensively studied based on the assumptions that the milling process dynamics is time invariant. However, nominal cutting parameters cannot guarantee the stab...The conventional prediction of milling stability has been extensively studied based on the assumptions that the milling process dynamics is time invariant. However, nominal cutting parameters cannot guarantee the stability of milling process at the shop floor level since there exists many uncertain factors in a practical manufacturing environment. This paper proposes a novel numerical method to estimate the upper and lower bounds of Lobe diagram, which is used to predict the milling stability in a robust way by taking into account the uncertain parameters of milling system. Time finite element method, a milling stability theory is adopted as the conventional deterministic model. The uncertain dynamics parameters are dealt with by the non-probabilistic model in which the parameters with uncertainties are assumed to be bounded and there is no need for probabilistic distribution densities functions. By doing so, interval instead of deterministic stability Lobe is obtained, which guarantees the stability of milling process in an uncertain milling environment, In the simulations, the upper and lower bounds of Lobe diagram obtained by the changes of modal parameters of spindle-tool system and cutting coefficients are given, respectively. The simulation results show that the proposed method is effective and can obtain satisfying bounds of Lobe diagrams. The proposed method is helpful for researchers at shop floor to making decision on machining parameters selection.展开更多
Because the existing spectral estimation methods for railway track irregularity analysis are very sensitive to outliers, a robust spectral estimation method is presented to process track irregularity signals. The prop...Because the existing spectral estimation methods for railway track irregularity analysis are very sensitive to outliers, a robust spectral estimation method is presented to process track irregularity signals. The proposed robust method is verified using 100 groups of clean/contaminated data reflecting he vertical profile irregularity taken from Bejing-Guangzhou railway with a sampling frequency of 33 data every ~10 m, and compared with the Auto Regressive (AR) model. The experimental results show that the proposed robust estimation is resistible to noise and insensitive to outliers, and is superior to the AR model in terms of efficiency, stability and reliability.展开更多
Point cloud registration is a fundamental task in both remote sensing,photogrammetry,and computer vision,which is to align multiple point clouds to the same coordinate frame.Especially in LiDAR odometry,by conducting ...Point cloud registration is a fundamental task in both remote sensing,photogrammetry,and computer vision,which is to align multiple point clouds to the same coordinate frame.Especially in LiDAR odometry,by conducting the transformation between two adjacent scans,the pose of the platform can be estimated.To be specific,the goal is to recover the relative six-degree-of-freedom(6 DoF)pose between the source point cloud and the target point cloud.In this paper,we explore the use of robust estimators in the phase correlation when registering two point clouds,enabling a 6 DoF pose estimation between point clouds in a sub-voxel accuracy.The estimator is a rule for calculating an estimate of a given quantity based on observed data.A robust estimator is an estimation rule that is insensitive to nonnormality and can estimate parameters of a given objective function from noisy observations.The proposed registration method is theoretically insensitive to noise and outliers than correspondence-based methods.Three core steps are involved in the method:transforming point clouds from the spatial domain to the frequency domain,decoupling of rotations and translations,and using robust estimators to estimate phase shifts.Since the estimation of transformation parameters lies in the calculation of phase shifts,robust estimators play a vital role in shift estimation accuracy.In this paper,we have tested the performance of six different robust estimators and provide comparisons and discussions on the contributions of robust estimators in the 3D phase correlation.Different point clouds from two urban scenarios and one indoor scene are tested.Results validate the proposed method can reach performance that predominant rotation and translation errors reaching less than 0.5°and 0.5 m,respectively.Moreover,the performance of various tested robust estimators is compared and discussed.展开更多
Load simulator is a key test equipment for aircraft actuation systems in hardware-in-the-loop-simulation. Static loading is an essential function of the load simulator and widely used in the static/dynamic stiffness t...Load simulator is a key test equipment for aircraft actuation systems in hardware-in-the-loop-simulation. Static loading is an essential function of the load simulator and widely used in the static/dynamic stiffness test of aircraft actuation systems. The tracking performance of the static loading is studied in this paper. Firstly, the nonlinear mathematical models of the hydraulic load simulator are derived, and the feedback linearization method is employed to construct a feed-forward controller to improve the force tracking performance. Considering the effect of the friction, a LuGre model based friction compensation is synthesized, in which the unmeasurable state is estimated by a dual state observer via a controlled learning mechanism to guarantee that the estimation is bounded. The modeling errors are attenuated by a well-designed robust controller with a control accuracy measured by a design parameter. Employing the dual state observer is to capture the different effects of the unmeasured state and hence can improve the friction compensation accuracy. The tracking performance is summarized by a derived theorem. Experimental results are also obtained to verify the high performance nature of the proposed control strategy.展开更多
An optimized robust filtering algorithm for uncertain discrete-time systemsis presented. To get a series of computational equations, the uncertain part generated by theuncertain systematic matrix in the expression of ...An optimized robust filtering algorithm for uncertain discrete-time systemsis presented. To get a series of computational equations, the uncertain part generated by theuncertain systematic matrix in the expression of the error-covariance matrix of time update stateestimation is optimized and the least upper bound of the uncertain part is given. By means of theseresults, the equivalent systematic matrix is obtained and a robust time update algorithm is builtup. On the other hand, uncertain parts generated by the uncertain observation matrix in theexpression of the error-covariance matrix of measurement update state estimation are optimized, andthe largest lower bound of the uncertain part is given. Thus both the time update and measurementupdate algorithms are developed. By means of the matrix inversion formula, the expression structuresof both time update and measurement update algorithms are all simplified. Moreover, the convergencecondition of a robust filter is developed to make the results easy to application. The results offlight data processing show that the method presented in this paper is efficient.展开更多
文摘In real-world applications, datasets frequently contain outliers, which can hinder the generalization ability of machine learning models. Bayesian classifiers, a popular supervised learning method, rely on accurate probability density estimation for classifying continuous datasets. However, achieving precise density estimation with datasets containing outliers poses a significant challenge. This paper introduces a Bayesian classifier that utilizes optimized robust kernel density estimation to address this issue. Our proposed method enhances the accuracy of probability density distribution estimation by mitigating the impact of outliers on the training sample’s estimated distribution. Unlike the conventional kernel density estimator, our robust estimator can be seen as a weighted kernel mapping summary for each sample. This kernel mapping performs the inner product in the Hilbert space, allowing the kernel density estimation to be considered the average of the samples’ mapping in the Hilbert space using a reproducing kernel. M-estimation techniques are used to obtain accurate mean values and solve the weights. Meanwhile, complete cross-validation is used as the objective function to search for the optimal bandwidth, which impacts the estimator. The Harris Hawks Optimisation optimizes the objective function to improve the estimation accuracy. The experimental results show that it outperforms other optimization algorithms regarding convergence speed and objective function value during the bandwidth search. The optimal robust kernel density estimator achieves better fitness performance than the traditional kernel density estimator when the training data contains outliers. The Naïve Bayesian with optimal robust kernel density estimation improves the generalization in the classification with outliers.
基金This project was supported by the National Natural Science Foundation of China (60572038)
文摘In practical multi-sensor information fusion systems, there exists uncertainty about the network structure, active state of sensors, and information itself （including fuzziness, randomness, incompleteness as well as roughness, etc）. Hence it requires investigating the problem of uncertain information fusion. Robust learning algorithm which adapts to complex environment and the fuzzy inference algorithm which disposes fuzzy information are explored to solve the problem. Based on the fusion technology of neural networks and fuzzy inference algorithm, a multi-sensor uncertain information fusion system is modeled. Also RANFIS learning algorithm and fusing weight synthesized inference algorithm are developed from the ANFIS algorithm according to the concept of robust neural networks. This fusion system mainly consists of RANFIS confidence estimator, fusing weight synthesized inference knowledge base and weighted fusion section. The simulation result demonstrates that the proposed fusion model and algorithm have the capability of uncertain information fusion, thus is obviously advantageous compared with the conventional Kalman weighted fusion algorithm.
文摘The parameter estimation problem in linear model is considered when multicollinearity and outliers exist simultaneously.A class of new estimators,robust general shrunken estimators,are proposed by grafting the robust estimation techniques philosophy into the biased estimator,and their statistical properties are discussed.By appropriate choices of the shrinking parameter matrix,we obtain many useful and important estimators.A numerical example is used to illustrate that these new estimators can not only effectively overcome difficulty caused by multicollinearity but also resist the influence of outliers.
基金supported by the National Natural Science of Foundation of China(42104008,42204006)the Jiangxi Provincial Natural Science Foundation(20232BAB213075)the Open Fund of Hubei Luojia Laboratory(grant 230100019,230100020)。
文摘The recovery of coseismic 3-dimensional(3D)surface deformation field plays a crucial role in studying seismic source characteristics and earthquake hazards.As of now,there are two main types of methods for recovering coseismic 3D surface deformations:the first type is to solve the problem directly by using the least squares method based on observations from three or more viewpoints,and the second type solves the problem by combining ascending and descending In SAR line-of-sight(LOS)observations with constraint models.The former type is mainly applicable to surface rupture earthquakes,because when an earthquake ruptures to the surface,we can usually obtain surface deformation observations from three or more views.The latter type applies to earthquakes that do not cause surface ruptures and have extensive blind faults.Currently,most research focuses on improving the above types of methods.However,some key factors in the coseismic 3D surface deformation inversion are rarely mentioned,such as the influence of window size on the inversion results in the strain model and variance component estimation method(SM-VCE),and whether the outliers in the observational data are considered.So,we developed a new chain of integrating In SAR observation and SM-VCE model to systematically assess the impacts of window size and outliers on coseismic 3D surface deformation inversions.Through simulation experiments,we observed that the selection of window size significantly impacts the accuracy of the results.Specifically,larger window sizes lead to wider residuals ranges in the fault region,resulting in the loss of extreme solution values when using a window of 15×15 pixels(i.e.,7.5 km×7.5 km).Hence,we recommend utilizing windows with 7×7 pixels or 9×9 pixels for optimal accuracy,as larger window sizes diminish the significance of the outcomes.The elimination of points displaying different deformation directions helps reduce residuals and preserves near-field deformation results.Furthermore,the residuals along the 3D direction can be reduced by 10%-30%when a small set of points is selected using a method based on Euclidean distances.However,when 441 points were selected,the vertical residuals increased by 22%compared to 81 points.Integration of the SM-VCE algorithm with robust estimation techniques effectively minimizes far-field deformation errors in data,thereby marginally enhancing the near-field deformation solution.
基金funded by the China Postdoctoral Science Foundation(No.2023M730337)。
文摘This study investigated the problems of non-cooperative target recognition and relative motion estimation during spacecraft rendezvous maneuvers.A structure integrating an Inertial Measurement Unit(IMU)and a visual camera was presented.The angular velocity output of the IMU was used to calculate the motion trajectories of star points in multiple image frames,which can highlight the motion of non-cooperative targets with respect to the image background to improve the probability of target recognition.To solve the problem of target misidentification caused by new star points entering the field of view,a target-tracking link based on IMU prediction was introduced to track the position of the target in the image.Furthermore,a measurement model was constructed using the line-of-sight vector generated from target recognition,and the relative motion state was estimated using a Huber-based non-linear filter.Semi-physical and numerical simulations were performed to evaluate the effectiveness and efficiency of the proposed method.
基金co-supported by the National Natural Science Foundation of China(No.61153002)the Aeronautical Science Foundation of China(No.20130153002)
文摘In micro-electro-mechanical system based inertial navigation system（MEMS-INS）/global position system（GPS） integrated navigation systems, there exist unknown disturbances and abnormal measurements. In order to obtain high estimation accuracy and enhance detection sensitivity to faults in measurements, this paper deals with the problem of model-based robust estimation（RE） and fault detection（FD）. A filter gain matrix and a post-filter are designed to obtain a RE and FD algorithm with current measurements, which is different from most of the existing priori filters using measurements in one-step delay. With the designed filter gain matrix, the H-infinity norm of the transfer function from noise inputs to estimation error outputs is limited within a certain range; with the designed post-filter, the residual signal is robust to disturbances but sensitive to faults. Therefore, the algorithm can guarantee small estimation errors in the presence of disturbances and have high sensitivity to faults. The proposed method is evaluated in an integrated navigation system, and the simulation results show that it is more effective in position estimation and fault signal detection than priori RE and FD algorithms.
文摘Depending on analyzing the abuse of equivalent weights,a set of self-contained theory system on robust estimation based on equivalent variance-covariance is established,which includes ρ function, φ function,equivalent variance-covariance function,influence function and breakdown point.And an example is given to verify that the robust models proposed in this paper are reliable and correct.
基金supported by the State Key Laboratory of Geo-Information Engineering(SKLGIE2022-Z-2-1)the National Natural Science Foundation of China(41674024,42174036).
文摘Once the spoofer has controlled the navigation sys-tem of unmanned aerial vehicle(UAV),it is hard to effectively control the error convergence to meet the threshold condition only by adjusting parameters of estimation if estimation of the spoofer on UAV has continuous observation error.Aiming at this problem,the influence of the spoofer’s state estimation error on spoofing effect and error convergence conditions is theoretically analyzed,and an improved adaptively robust estimation algo-rithm suitable for steady-state linear quadratic estimator is pro-posed.It enables the spoofer’s estimator to reliably estimate UAV status in real time,improves the robustness of the estima-tor in responding to observation errors,and accelerates the con-vergence time of error control.Simulation experiments show that the mean value of normalized innovation squared(NIS)is reduced by 88.5%,and the convergence time of NIS value is reduced by 76.3%,the convergence time of true trajectory error of UAV is reduced by 42.3%,the convergence time of estimated trajectory error of UAV is reduced by 67.4%,the convergence time of estimated trajectory error of the spoofer is reduced by 33.7%,and the convergence time of broadcast trajectory error of the spoofer is reduced by 54.8%when the improved algorithm is used.The improved algorithm can make UAV deviate from pre-set trajectory to spoofing trajectory more effectively and more subtly.
基金Supported by the Funds for 0utstanding Young Researchers from the National Natural Science Foundation of China （No.60025308） and the Key Technologies R＆D Program in the National ＂10th 5-year Plan＂ （No.2001BA204B07）.
文摘Data reconciliation is an effective technique for providing accurate and consistent value for chemical process. However, the presence of gross errors can severely bias the reconciled results. Robust estimators can significantly reduce the effect of gross errors and yield less-biased results. In this article, a new method is proposed to solve the robust data reconciliation problem of nonlinear chemical process. By using several technologies including linearization method, penalty function, virtual observation equation, and equivalent weights method, the robust data reconciliation problem can be transformed into least squares estimator problem which leads to the convenience in computation. Simulation results in a nonlinear chemical process demonstrate the efficiency of the proposed method.
基金supported by the National Natural Science Foundation of China under Grants 61372092"863" Program under Grants 2014AA01A701
文摘Full duplex radio increases the frequency efficiency but its performance is limited by the self-interference （SI）. We first analyze the multiple noises in the full duplex radio system and model such noises as an α - stable distribution. Then we formulate a novel non-Gaussian SI problem. Under the maximum correntropy criterion （MCC）, a robust digital non-linear self-interference cancellation algorithm is proposed for the SI channel estimation. A gradient descent based algorithm is derived to search the optimal solution. Simulation results show that the proposed algorithm can achieve a smaller estimation error and a higher pseudo signal to interference plus noise ratio （PSINR） than the well-known least mean square （LMS） algorithm and least square （LS） algorithm.
文摘This paper proposes a new robust video stabilization algorithm to remove unwanted vibrations in video sequences. A complete theoretical analysis is first established for video stabilization, providing a basis for new stabilization algorithm. Secondly, a new robust global motion estimation (GME) algorithm is proposed. Different from classic methods, the GME algorithm is based on spatial-temporal filtered motion vectors computed by block-matching methods. In addition, effective schemes are employed in correction phase to prevent boundary artifacts and error accumulation. Experiments show that the proposed algorithm has satisfactory stabilization effects while maintaining good tradeoff between speed and precision.
基金supported by the National 863 project(2013AA122501-1)the National Natural Science Foundation of China(41020144004,41474015,41374019,41374003,41274040)
文摘Integrity is significant for safety-of-life applications. Receiver autonomous integrity monitoring（RAIM） has been developed to provide integrity service for civil aviation. At first,the conventional RAIM algorithm is only suitable for single fault detection, single GNSS constellation. However, multiple satellite failure should be considered when more than one satellite navigation system are adopted. To detect and exclude multi-fault, most current algorithms perform an iteration procedure considering all possible fault model which lead to heavy computation burden. An alternative RAIM is presented in this paper based on multiple satellite constellations（for example, GPS and Bei Dou（BDS） etc.） and robust estimation for multi-fault detection and exclusion, which can not only detect multi-failures,but also control the influences of near failure observation. Besides, the RAIM algorithm based on robust estimation is more efficient than the current RAIM algorithm for multiple constellation and multiple faults. Finally, the algorithm is tested by GPS/Bei Dou data.
基金supported by the National Key Research and Development Project of China(2018YFE0122200).
文摘A robust low-carbon economic optimal scheduling method that considers source-load uncertainty and hydrogen energy utilization is developed.The proposed method overcomes the challenge of source-load random fluctuations in integrated energy systems(IESs)in the operation scheduling problem of integrated energy production units(IEPUs).First,to solve the problem of inaccurate prediction of renewable energy output,an improved robust kernel density estimation method is proposed to construct a data-driven uncertainty output set of renewable energy sources statistically and build a typical scenario of load uncertainty using stochastic scenario reduction.Subsequently,to resolve the problem of insufficient utilization of hydrogen energy in existing IEPUs,a robust low-carbon economic optimal scheduling model of the source-load interaction of an IES with a hydrogen energy system is established.The system considers the further utilization of energy using hydrogen energy coupling equipment(such as hydrogen storage devices and fuel cells)and the comprehensive demand response of load-side schedulable resources.The simulation results show that the proposed robust stochastic optimization model driven by data can effectively reduce carbon dioxide emissions,improve the source-load interaction of the IES,realize the efficient use of hydrogen energy,and improve system robustness.
基金supported in part by the 973 Program(Nos.2009CB320602,2012CB316504)in part by the National Natural Science Foundation of China(Nos.61174122,61021063,60721003,60625305)in part by the Specialized Research Fund for the Doctoral Program of Higher Education,China(No.20110002110045)
文摘In this paper, we investigate state estimations of a dynamical system in which not only process and measurement noise, but also parameter uncertainties and deterministic input signals are involved. The sensitivity penalization based robust state estimation is extended to uncertain linear systems with deterministic input signals and parametric uncertainties which may nonlinearly affect a state-space plant model. The form of the derived robust estimator is similar to that of the well-known Kalman filter with a comparable computational complexity. Under a few weak assumptions, it is proved that though the derived state estimator is biased, the bound of estimation errors is finite and the covariance matrix of estimation errors is bounded. Numerical simulations show that the obtained robust filter has relatively nice estimation performances.
基金This research was supported by the National Natural Science Foundation of China[grant number 41471360]the Fundamental Research Funds for the Central Universities[grant number 2652015176].
文摘Based on Bayesian theory and RANSAC,this paper applies Bayesian Sampling Consensus(BaySAC)method using convergence evaluation of hypothesis models in indoor point cloud processing.We implement a conditional sampling method,BaySAC,to always select the minimum number of required data with the highest inlier probabilities.Because the primitive parameters calculated by the different inlier sets should be convergent,this paper presents a statistical testing algorithm for a candidate model parameter histogram to compute the prior probability of each data point.Moreover,the probability update is implemented using the simplified Bayes’formula.The performances of the BaySAC algorithm with the proposed strategies of the prior probability determination and the RANSAC framework are compared using real data-sets.The experimental results indicate that the more outliers contain the data points,the higher computational efficiency of our proposed algorithm gains compared with RANSAC.The results also indicate that the proposed statistical testing strategy can determine sound prior inlier probability free of the change of hypothesis models.
基金supported by National Basic Research Program of China (973 Program, Grant No. 2011CB706804)National Natural Science Foundation of China (Grant Nos. 50835004, 51005087)
文摘The conventional prediction of milling stability has been extensively studied based on the assumptions that the milling process dynamics is time invariant. However, nominal cutting parameters cannot guarantee the stability of milling process at the shop floor level since there exists many uncertain factors in a practical manufacturing environment. This paper proposes a novel numerical method to estimate the upper and lower bounds of Lobe diagram, which is used to predict the milling stability in a robust way by taking into account the uncertain parameters of milling system. Time finite element method, a milling stability theory is adopted as the conventional deterministic model. The uncertain dynamics parameters are dealt with by the non-probabilistic model in which the parameters with uncertainties are assumed to be bounded and there is no need for probabilistic distribution densities functions. By doing so, interval instead of deterministic stability Lobe is obtained, which guarantees the stability of milling process in an uncertain milling environment, In the simulations, the upper and lower bounds of Lobe diagram obtained by the changes of modal parameters of spindle-tool system and cutting coefficients are given, respectively. The simulation results show that the proposed method is effective and can obtain satisfying bounds of Lobe diagrams. The proposed method is helpful for researchers at shop floor to making decision on machining parameters selection.
文摘Because the existing spectral estimation methods for railway track irregularity analysis are very sensitive to outliers, a robust spectral estimation method is presented to process track irregularity signals. The proposed robust method is verified using 100 groups of clean/contaminated data reflecting he vertical profile irregularity taken from Bejing-Guangzhou railway with a sampling frequency of 33 data every ~10 m, and compared with the Auto Regressive (AR) model. The experimental results show that the proposed robust estimation is resistible to noise and insensitive to outliers, and is superior to the AR model in terms of efficiency, stability and reliability.
基金National Key Research and Development Program of China under Project(No.2018YFB0505400)National Natural Science Foundation of China(No.41631178)。
文摘Point cloud registration is a fundamental task in both remote sensing,photogrammetry,and computer vision,which is to align multiple point clouds to the same coordinate frame.Especially in LiDAR odometry,by conducting the transformation between two adjacent scans,the pose of the platform can be estimated.To be specific,the goal is to recover the relative six-degree-of-freedom(6 DoF)pose between the source point cloud and the target point cloud.In this paper,we explore the use of robust estimators in the phase correlation when registering two point clouds,enabling a 6 DoF pose estimation between point clouds in a sub-voxel accuracy.The estimator is a rule for calculating an estimate of a given quantity based on observed data.A robust estimator is an estimation rule that is insensitive to nonnormality and can estimate parameters of a given objective function from noisy observations.The proposed registration method is theoretically insensitive to noise and outliers than correspondence-based methods.Three core steps are involved in the method:transforming point clouds from the spatial domain to the frequency domain,decoupling of rotations and translations,and using robust estimators to estimate phase shifts.Since the estimation of transformation parameters lies in the calculation of phase shifts,robust estimators play a vital role in shift estimation accuracy.In this paper,we have tested the performance of six different robust estimators and provide comparisons and discussions on the contributions of robust estimators in the 3D phase correlation.Different point clouds from two urban scenarios and one indoor scene are tested.Results validate the proposed method can reach performance that predominant rotation and translation errors reaching less than 0.5°and 0.5 m,respectively.Moreover,the performance of various tested robust estimators is compared and discussed.
基金National Science Fund for Distinguished Young Scholars (50825502)
文摘Load simulator is a key test equipment for aircraft actuation systems in hardware-in-the-loop-simulation. Static loading is an essential function of the load simulator and widely used in the static/dynamic stiffness test of aircraft actuation systems. The tracking performance of the static loading is studied in this paper. Firstly, the nonlinear mathematical models of the hydraulic load simulator are derived, and the feedback linearization method is employed to construct a feed-forward controller to improve the force tracking performance. Considering the effect of the friction, a LuGre model based friction compensation is synthesized, in which the unmeasurable state is estimated by a dual state observer via a controlled learning mechanism to guarantee that the estimation is bounded. The modeling errors are attenuated by a well-designed robust controller with a control accuracy measured by a design parameter. Employing the dual state observer is to capture the different effects of the unmeasured state and hence can improve the friction compensation accuracy. The tracking performance is summarized by a derived theorem. Experimental results are also obtained to verify the high performance nature of the proposed control strategy.
基金Chinese Excellent Youth Science F oundation ( 6992 5 3 0 6) and Aeronautical Foundation Project
文摘An optimized robust filtering algorithm for uncertain discrete-time systemsis presented. To get a series of computational equations, the uncertain part generated by theuncertain systematic matrix in the expression of the error-covariance matrix of time update stateestimation is optimized and the least upper bound of the uncertain part is given. By means of theseresults, the equivalent systematic matrix is obtained and a robust time update algorithm is builtup. On the other hand, uncertain parts generated by the uncertain observation matrix in theexpression of the error-covariance matrix of measurement update state estimation are optimized, andthe largest lower bound of the uncertain part is given. Thus both the time update and measurementupdate algorithms are developed. By means of the matrix inversion formula, the expression structuresof both time update and measurement update algorithms are all simplified. Moreover, the convergencecondition of a robust filter is developed to make the results easy to application. The results offlight data processing show that the method presented in this paper is efficient.
