This article considers delay dependent decentralized H∞ filtering for a class of uncertain interconnected systems, where the uncertainties are assumed to be time varying and satisfy the norm-bounded conditions. First...This article considers delay dependent decentralized H∞ filtering for a class of uncertain interconnected systems, where the uncertainties are assumed to be time varying and satisfy the norm-bounded conditions. First, combining the Lyapunov-Krasovskii functional approach and the delay integral inequality of matrices, a sufficient condition of the existence of the robust decentralized H∞ filter is derived, which makes the error systems asymptotically stable and satisfies the H∞ norm of the transfer function from noise input to error output less than the specified up-bound on the basis of the form of uncertainties. Then, the above sufficient condition is transformed to a system of easily solvable LMIs via a series of equivalent transformation. Finally, the numerical simulation shows the efficiency of the main results.展开更多
This paper is concerned with the robust H ∞ filter problem for networked environments, which are subject to both transmission delay and packet dropouts randomly. By employing random series which have Bernoulli distri...This paper is concerned with the robust H ∞ filter problem for networked environments, which are subject to both transmission delay and packet dropouts randomly. By employing random series which have Bernoulli distributions taking value of 0 or 1, the data transmission model is obtained. Based on state augmentation and stochastic theory, the sufficient condition for robust stability with H ∞ constraints is derived for the filtering error system. The robust filter is designed in terms of feasibility of one certain linear matrix inequality (LMI), which is formed by adopting matrix congruence transformations. A numerical example is given to show the effectiveness of the proposed filtering method.展开更多
In this paper , the principle of H∞ filtering is discussed and H_∞ filter is constructed, which is used in the initial alignment of the strapdown inertial navigation systems(SINS). The error model of SINS is derived...In this paper , the principle of H∞ filtering is discussed and H_∞ filter is constructed, which is used in the initial alignment of the strapdown inertial navigation systems(SINS). The error model of SINS is derived. By utilizing constructed H∞ filter, the filtering calculation to that system has been conducted. The simulation results of the misalignment angle are given under the condition of unknown noises. The results show that the process of alignment with H∞ filter is much faster and with excellent robustness.展开更多
This paper is concerned with the non-fragile H∞ filter design problem for uncertain discrete-time Takagi-Sugeno (T-S) fuzzy systems with time delay. To begin with, the T-S fuzzy system is transformed to an equivale...This paper is concerned with the non-fragile H∞ filter design problem for uncertain discrete-time Takagi-Sugeno (T-S) fuzzy systems with time delay. To begin with, the T-S fuzzy system is transformed to an equivalent switching fuzzy system. Then, based on the piecewise Lyapunov function and matrix decoupling technique, a new delay-dependent non-fragile H∞ filtering method is proposed for the switching fuzzy system. The proposed condition is less conservative than the previous results. Since only a set of LMIs is involved, the filter parameters can be solved directly. Finally, a design example is provided to illustrate the validity of the proposed method.展开更多
The robust H∞ filtering problem for uncertain discrete-time Markovian jump linear systems with mode- dependent time-delays is investigated. Attention is focused on designing a Markovian jump linear filter that ensure...The robust H∞ filtering problem for uncertain discrete-time Markovian jump linear systems with mode- dependent time-delays is investigated. Attention is focused on designing a Markovian jump linear filter that ensures robust stochastic stability while achieving a prescribed H∞ performance level of the resulting filtering error system, for all admissible uncertainties. The key features of the approach include the introduction of a new type of stochastic Lyapunov functional and some free weighting matrix variables. Sufficient conditions for the solvability of this problem are obtained in terms of a set of linear matrix inequalities. Numerical examples are provided to demonstrate the reduced conservatism of the proposed approach.展开更多
This paper investigates robust filter design for linear discrete-time impulsive systems with uncertainty under H∞ performance. First, an impulsive linear filter and a robust H∞ filtering problem are introduced for a...This paper investigates robust filter design for linear discrete-time impulsive systems with uncertainty under H∞ performance. First, an impulsive linear filter and a robust H∞ filtering problem are introduced for a discrete-time impulsive systems. Then, a sufficient condition of asymptotical stability and H∞ performance for the filtering error systems are provided by the discrete-time Lyapunov function method. The filter gains can be obtained by solving a set of linear matrix inequalities (LMIs). Finally, a numerical example is presented to show effectiveness of the obtained result.展开更多
The problem of H∞ filtering for polytopic Delta operator linear systems is investigated. An improved H∞ performance criterion is presented based on the bounded real lemma. Upon the improved performance criterion, a ...The problem of H∞ filtering for polytopic Delta operator linear systems is investigated. An improved H∞ performance criterion is presented based on the bounded real lemma. Upon the improved performance criterion, a sufficient condition for the existence of parameter-dependent H∞ filtering is derived in terms of linear matrix inequalities. The designed filter can be obtained from the solution of a convex optimization problem. The filter design makes full use of the parameter-dependent approach, which leads to a less conservative result than conventional design methods. A numerical example is given to illustrate the effectiveness of the proposed approach.展开更多
We consider the robust H 2/H ∞ filtering problem for linear perturbed systems with steadystate error variance assignment. The generalized inverse technique of matrix is introduced, and a new algorithm is developed....We consider the robust H 2/H ∞ filtering problem for linear perturbed systems with steadystate error variance assignment. The generalized inverse technique of matrix is introduced, and a new algorithm is developed. After two Riccati equations are solved, the filter can be obtained directly, and the following three performance requirements are simultaneously satisfied: The filtering process is asymptotically stable; the steadystate variance of the estimation error of each state is not more than the individual prespecified upper bound; the transfer function from exogenous noise inputs to error state outputs meets the prespecified H ∞ norm upper bound constraint. A numerical example is provided to demonstrate the flexibility of the proposed design approach.展开更多
The problem of nonlinear H∞ filtering for interconnected Markovian jump systems is discussed. The aim of this note is the design of a nonlinear Markovian jump filter such that the resulting error system is exponentia...The problem of nonlinear H∞ filtering for interconnected Markovian jump systems is discussed. The aim of this note is the design of a nonlinear Markovian jump filter such that the resulting error system is exponentially meansquare stable and ensures a prescribed H∞ performance. A sufficient condition for the solvability of this problem is given in terms of linear matrix inequalities(LMIs). A simulation example is presented to demonstrate the effectiveness of the proposed design approach.展开更多
A novel Krein space approach to robust H∞ filtering for linear uncertain systems is developed. The parameter uncertainty, entering into both states and measurement equations, satisfies an energy-type constraint. Then...A novel Krein space approach to robust H∞ filtering for linear uncertain systems is developed. The parameter uncertainty, entering into both states and measurement equations, satisfies an energy-type constraint. Then a Krein space approach is used to tackle the robust H∞ filtering problem. To this end, a new Krein space formal system is designed according to the original sum quadratic constraint (SQC) without introducing any nonzero factors into it and, consequently, the estimate recursion is obtained through the filter gain in Krein space. Finally, a numerical example is given to demonstrate the effectiveness of the proposed approach.展开更多
An H∞ filter design for linear time delay system with randomly varying sensor delay is investigated.The delay considered here is assumed to satisfy a certain stochastic characteristic.A stochastic variable satisfying...An H∞ filter design for linear time delay system with randomly varying sensor delay is investigated.The delay considered here is assumed to satisfy a certain stochastic characteristic.A stochastic variable satisfying Bernoulli random binary distribution is introduced and a new system model is established by employing the measurements with random delay.By using the linear matrix inequality(LMI) technique,sufficient conditions are derived for ensuring the mean-square stochastic stability of the filtering error systems and guaranteeing a prescribed H∞ filtering performance.Finally,a numerical example is given to demonstrate the effectiveness of the proposed approach.展开更多
The problem of robust H∞ filtering for a class of neutral jump systems with time-delay and norm-bounded uncertainties is considered. By re-constructing the system, the dynamics of overall augmented error systems is o...The problem of robust H∞ filtering for a class of neutral jump systems with time-delay and norm-bounded uncertainties is considered. By re-constructing the system, the dynamics of overall augmented error systems is obtained which involves unknown inputs represented by disturbances, model uncertainties and time-delays. As to the nominal system, sufficient conditions are provided for the existence of the mode-dependent H∞ filter by selecting the appropriate Lyapunov-Krasovskii function and the robust H∞ filter is proposed for the jump system while considering the time-delays and uncertainties. Both of above conditions for the existence of the H∞ filter and roust H∞ filter are presented in terms of linear matrix inequalities, and convex optimization problems are formulated to design the desired filters. By employing the proposed mode-dependent H∞ filter, the systems have the stochastic stability and better ability of restraining disturbances stochastically, and the given prescribed H∞ performance is guaranteed. Simulation results illustrate the effectiveness of developed techniques.展开更多
To solve the problem of data fusion for prior information such as track information and train status in train positioning,an adaptive H∞filtering algorithm with combination constraint is proposed,which fuses prior in...To solve the problem of data fusion for prior information such as track information and train status in train positioning,an adaptive H∞filtering algorithm with combination constraint is proposed,which fuses prior information with other sensor information in the form of constraints.Firstly,the train precise track constraint method of the train is proposed,and the plane position constraint and train motion state constraints are analysed.A model for combining prior information with constraints is established.Then an adaptive H∞filter with combination constraints is derived based on the adaptive adjustment method of the robustness factor.Finally,the positioning effect of the proposed algorithm is simulated and analysed under the conditions of a straight track and a curved track.The results show that the positioning accuracy of the algorithm with constrained filtering is significantly better than that of the algorithm without constrained filtering and that the algorithm with constrained filtering can achieve better performance when combined with track and condition information,which can significantly reduce the train positioning error.The effectiveness of the proposed algorithm is verified.展开更多
In this paper,the recursive filtering problem is considered for stochastic systems over filter-and-forward successive relay(FFSR)networks.An FFSR is located between the sensor and the remote filter to forward the meas...In this paper,the recursive filtering problem is considered for stochastic systems over filter-and-forward successive relay(FFSR)networks.An FFSR is located between the sensor and the remote filter to forward the measurement.In the successive relay,two cooperative relay nodes are adopted to forward the signals alternatively,thereby existing switching characteristics and inter-relay interferences(IRI).Since the filter-and-forward scheme is employed,the signal received by the relay is retransmitted after it passes through a linear filter.The objective of the paper is to concurrently design optimal recursive filters for FFSR and stochastic systems against switching characteristics and IRI of relays.First,a uniform measurement model is proposed by analyzing the transmission mechanism of FFSR.Then,novel filter structures with switching parameters are constructed for both FFSR and stochastic systems.With the help of the inductive method,filtering error covariances are presented in the form of coupled difference equations.Next,the desired filter gain matrices are further obtained by minimizing the trace of filtering error covariances.Moreover,the stability performance of the filtering algorithm is analyzed where the uniform bound is guaranteed on the filtering error covariance.Finally,the effectiveness of the proposed filtering method over FFSR is verified by a three-order resistance-inductance-capacitance circuit system.展开更多
In the era of exponential growth of data availability,the architecture of systems has a trend toward high dimensionality,and directly exploiting holistic information for state inference is not always computationally a...In the era of exponential growth of data availability,the architecture of systems has a trend toward high dimensionality,and directly exploiting holistic information for state inference is not always computationally affordable.This paper proposes a novel Bayesian filtering algorithm that considers algorithmic computational cost and estimation accuracy for high-dimensional linear systems.The high-dimensional state vector is divided into several blocks to save computation resources by avoiding the calculation of error covariance with immense dimensions.After that,two sequential states are estimated simultaneously by introducing an auxiliary variable in the new probability space,mitigating the performance degradation caused by state segmentation.Moreover,the computational cost and error covariance of the proposed algorithm are analyzed analytically to show its distinct features compared with several existing methods.Simulation results illustrate that the proposed Bayesian filtering can maintain a higher estimation accuracy with reasonable computational cost when applied to high-dimensional linear systems.展开更多
This paper focuses on the quadratic nonfragile filtering problem for linear non-Gaussian systems under multiplicative noises,multiple missing measurements as well as the dynamic event-triggered transmission scheme.The...This paper focuses on the quadratic nonfragile filtering problem for linear non-Gaussian systems under multiplicative noises,multiple missing measurements as well as the dynamic event-triggered transmission scheme.The multiple missing measurements are characterized through random variables that obey some given probability distributions,and thresholds of the dynamic event-triggered scheme can be adjusted dynamically via an auxiliary variable.Our attention is concentrated on designing a dynamic event-triggered quadratic nonfragile filter in the well-known minimum-variance sense.To this end,the original system is first augmented by stacking its state/measurement vectors together with second-order Kronecker powers,thus the original design issue is reformulated as that of the augmented system.Subsequently,we analyze statistical properties of augmented noises as well as high-order moments of certain random parameters.With the aid of two well-defined matrix difference equations,we not only obtain upper bounds on filtering error covariances,but also minimize those bounds via carefully designing gain parameters.Finally,an example is presented to explain the effectiveness of this newly established quadratic filtering algorithm.展开更多
The most critical part of a neutron computed tomography(NCT) system is the image processing algorithm,which directly affects the quality and speed of the reconstructed images.Various types of noise in the system can d...The most critical part of a neutron computed tomography(NCT) system is the image processing algorithm,which directly affects the quality and speed of the reconstructed images.Various types of noise in the system can degrade the quality of the reconstructed images.Therefore,to improve the quality of the reconstructed images of NCT systems,efficient image processing algorithms must be used.The anisotropic diffusion filtering(ADF) algorithm can not only effectively suppress the noise in the projection data,but also preserve the image edge structure information by reducing the diffusion at the image edges.Therefore,we propose the application of the ADF algorithm for NCT image reconstruction.To compare the performance of different algorithms in NCT systems,we reconstructed images using the ordered subset simultaneous algebraic reconstruction technique(OS-SART) algorithm with different regular terms as image processing algorithms.In the iterative reconstruction,we selected two image processing algorithms,the Total Variation and split Bregman solved total variation algorithms,for comparison with the performance of the ADF algorithm.Additionally,the filtered back-projection algorithm was used for comparison with an iterative algorithm.By reconstructing the projection data of the numerical and clock models,we compared and analyzed the effects of each algorithm applied in the NCT system.Based on the reconstruction results,OS-SART-ADF outperformed the other algorithms in terms of denoising,preserving the edge structure,and suppressing artifacts.For example,when the 3D Shepp–Logan was reconstructed at 25 views,the root mean square error of OS-SART-ADF was the smallest among the four iterative algorithms,at only 0.0292.The universal quality index,mean structural similarity,and correlation coefficient of the reconstructed image were the largest among all algorithms,with values of 0.9877,0.9878,and 0.9887,respectively.展开更多
We investigated the electric controllable spin-filtering effect in a zigzag phosphorene nanoribbon(ZPNR) based normal–antiferromagnet–normal junction. Two ferromagnets are closely coupled to the edges of the nanorib...We investigated the electric controllable spin-filtering effect in a zigzag phosphorene nanoribbon(ZPNR) based normal–antiferromagnet–normal junction. Two ferromagnets are closely coupled to the edges of the nanoribbon and form the edge-to-edge antiferromagnetism. Under an in-plane electric field, the two degenerate edge bands of the edge-to-edge antiferromagnet split into four spin-polarized sub-bands and a 100% spin-polarized current can be easily induced with the maximal conductance 2e~2/h. The spin polarization changes with the strength of the electric field and the exchange field,and changes sign at opposite electric fields. The spin-polarized current switches from one edge to the other by reversing the direction of the electric field. The edge current can also be controlled spatially by changing the electric potential of the scattering region. The manipulation of edge current is useful in spin-transfer-torque magnetic random-access memory and provides a practical way to develop controllable spintronic devices.展开更多
Passive detection of low-slow-small(LSS)targets is easily interfered by direct signal and multipath clutter,and the traditional clutter suppression method has the contradiction between step size and convergence rate.I...Passive detection of low-slow-small(LSS)targets is easily interfered by direct signal and multipath clutter,and the traditional clutter suppression method has the contradiction between step size and convergence rate.In this paper,a frequency domain clutter suppression algorithm based on sparse adaptive filtering is proposed.The pulse compression operation between the error signal and the input reference signal is added to the cost function as a sparsity constraint,and the criterion for filter weight updating is improved to obtain a purer echo signal.At the same time,the step size and penalty factor are brought into the adaptive iteration process,and the input data is used to drive the adaptive changes of parameters such as step size.The proposed algorithm has a small amount of calculation,which improves the robustness to parameters such as step size,reduces the weight error of the filter and has a good clutter suppression performance.展开更多
基金the National Natural Science Foundation of China (60634020)the Hunan Provincial Natural Science Foundation of China (07JJ6138)+1 种基金the Postdoctoral Science Foundation of China (20060390883)the China Ph.D. Discipline Special Foundation (20050533028).
文摘This article considers delay dependent decentralized H∞ filtering for a class of uncertain interconnected systems, where the uncertainties are assumed to be time varying and satisfy the norm-bounded conditions. First, combining the Lyapunov-Krasovskii functional approach and the delay integral inequality of matrices, a sufficient condition of the existence of the robust decentralized H∞ filter is derived, which makes the error systems asymptotically stable and satisfies the H∞ norm of the transfer function from noise input to error output less than the specified up-bound on the basis of the form of uncertainties. Then, the above sufficient condition is transformed to a system of easily solvable LMIs via a series of equivalent transformation. Finally, the numerical simulation shows the efficiency of the main results.
基金supported by National Natural Science Foundation of China (No. 61004088)the Key Foundation for Basic Research from Science and Technology Commission of Shanghai (No. 09JC1408000)the Aeronautic Science Foundation of China (No. 20100157001)
文摘This paper is concerned with the robust H ∞ filter problem for networked environments, which are subject to both transmission delay and packet dropouts randomly. By employing random series which have Bernoulli distributions taking value of 0 or 1, the data transmission model is obtained. Based on state augmentation and stochastic theory, the sufficient condition for robust stability with H ∞ constraints is derived for the filtering error system. The robust filter is designed in terms of feasibility of one certain linear matrix inequality (LMI), which is formed by adopting matrix congruence transformations. A numerical example is given to show the effectiveness of the proposed filtering method.
文摘In this paper , the principle of H∞ filtering is discussed and H_∞ filter is constructed, which is used in the initial alignment of the strapdown inertial navigation systems(SINS). The error model of SINS is derived. By utilizing constructed H∞ filter, the filtering calculation to that system has been conducted. The simulation results of the misalignment angle are given under the condition of unknown noises. The results show that the process of alignment with H∞ filter is much faster and with excellent robustness.
基金supported by National Natural Science Foundation of China(No.60974139,No.60804021)Fundamental Research Funds for the Central Universities
文摘This paper is concerned with the non-fragile H∞ filter design problem for uncertain discrete-time Takagi-Sugeno (T-S) fuzzy systems with time delay. To begin with, the T-S fuzzy system is transformed to an equivalent switching fuzzy system. Then, based on the piecewise Lyapunov function and matrix decoupling technique, a new delay-dependent non-fragile H∞ filtering method is proposed for the switching fuzzy system. The proposed condition is less conservative than the previous results. Since only a set of LMIs is involved, the filter parameters can be solved directly. Finally, a design example is provided to illustrate the validity of the proposed method.
文摘The robust H∞ filtering problem for uncertain discrete-time Markovian jump linear systems with mode- dependent time-delays is investigated. Attention is focused on designing a Markovian jump linear filter that ensures robust stochastic stability while achieving a prescribed H∞ performance level of the resulting filtering error system, for all admissible uncertainties. The key features of the approach include the introduction of a new type of stochastic Lyapunov functional and some free weighting matrix variables. Sufficient conditions for the solvability of this problem are obtained in terms of a set of linear matrix inequalities. Numerical examples are provided to demonstrate the reduced conservatism of the proposed approach.
基金supported by the National Natural Science Foundation of China (No. 60874027)
文摘This paper investigates robust filter design for linear discrete-time impulsive systems with uncertainty under H∞ performance. First, an impulsive linear filter and a robust H∞ filtering problem are introduced for a discrete-time impulsive systems. Then, a sufficient condition of asymptotical stability and H∞ performance for the filtering error systems are provided by the discrete-time Lyapunov function method. The filter gains can be obtained by solving a set of linear matrix inequalities (LMIs). Finally, a numerical example is presented to show effectiveness of the obtained result.
文摘The problem of H∞ filtering for polytopic Delta operator linear systems is investigated. An improved H∞ performance criterion is presented based on the bounded real lemma. Upon the improved performance criterion, a sufficient condition for the existence of parameter-dependent H∞ filtering is derived in terms of linear matrix inequalities. The designed filter can be obtained from the solution of a convex optimization problem. The filter design makes full use of the parameter-dependent approach, which leads to a less conservative result than conventional design methods. A numerical example is given to illustrate the effectiveness of the proposed approach.
文摘We consider the robust H 2/H ∞ filtering problem for linear perturbed systems with steadystate error variance assignment. The generalized inverse technique of matrix is introduced, and a new algorithm is developed. After two Riccati equations are solved, the filter can be obtained directly, and the following three performance requirements are simultaneously satisfied: The filtering process is asymptotically stable; the steadystate variance of the estimation error of each state is not more than the individual prespecified upper bound; the transfer function from exogenous noise inputs to error state outputs meets the prespecified H ∞ norm upper bound constraint. A numerical example is provided to demonstrate the flexibility of the proposed design approach.
文摘The problem of nonlinear H∞ filtering for interconnected Markovian jump systems is discussed. The aim of this note is the design of a nonlinear Markovian jump filter such that the resulting error system is exponentially meansquare stable and ensures a prescribed H∞ performance. A sufficient condition for the solvability of this problem is given in terms of linear matrix inequalities(LMIs). A simulation example is presented to demonstrate the effectiveness of the proposed design approach.
基金supported by the National Natural Science Foundation of China (51179039)the Ph.D. Programs Foundation of Ministry of Education of China (20102304110021)
文摘A novel Krein space approach to robust H∞ filtering for linear uncertain systems is developed. The parameter uncertainty, entering into both states and measurement equations, satisfies an energy-type constraint. Then a Krein space approach is used to tackle the robust H∞ filtering problem. To this end, a new Krein space formal system is designed according to the original sum quadratic constraint (SQC) without introducing any nonzero factors into it and, consequently, the estimate recursion is obtained through the filter gain in Krein space. Finally, a numerical example is given to demonstrate the effectiveness of the proposed approach.
基金Supported by National Young Science Foundation of P.R.China(60604003)National Natural Science Key Foundation of P.R.China(60434020)National Key Technologies Research and Development Program in the 10th Five-year Plan(2001BA204B01)
文摘这份报纸处理与州的时间延期,参数无常和未知统计特征,但是与有限力量骚乱为 Lurie 单个系统的一个班过滤的柔韧的 H 的问题,试图设计一个要用体力地稳定的过滤器以便单个系统是的不明确的 Lurie 时间延期不仅常规,免费、稳定的推动,而且为所有可被考虑的无常为过滤错误动力学有 H 性能的规定水平。为如此的一个过滤器的存在的一个足够的条件以线性矩阵不平等(LMI ) 被建议。当 LMI 的这个集合的一个答案存在时,一个需要的过滤器的参量的矩阵能容易用 LMI 工具箱被获得。
基金National Natural Science Foundations of China (No. 60474079,No. 60704024,No. 60774060,No. 61074025,and No. 61074024)
文摘An H∞ filter design for linear time delay system with randomly varying sensor delay is investigated.The delay considered here is assumed to satisfy a certain stochastic characteristic.A stochastic variable satisfying Bernoulli random binary distribution is introduced and a new system model is established by employing the measurements with random delay.By using the linear matrix inequality(LMI) technique,sufficient conditions are derived for ensuring the mean-square stochastic stability of the filtering error systems and guaranteeing a prescribed H∞ filtering performance.Finally,a numerical example is given to demonstrate the effectiveness of the proposed approach.
基金Sponsored by the National Natural Science Foundation of China(Grant No.60574001)Program for New Century Excellent Talents in University(Grant No.NCET-05-0485)
文摘The problem of robust H∞ filtering for a class of neutral jump systems with time-delay and norm-bounded uncertainties is considered. By re-constructing the system, the dynamics of overall augmented error systems is obtained which involves unknown inputs represented by disturbances, model uncertainties and time-delays. As to the nominal system, sufficient conditions are provided for the existence of the mode-dependent H∞ filter by selecting the appropriate Lyapunov-Krasovskii function and the robust H∞ filter is proposed for the jump system while considering the time-delays and uncertainties. Both of above conditions for the existence of the H∞ filter and roust H∞ filter are presented in terms of linear matrix inequalities, and convex optimization problems are formulated to design the desired filters. By employing the proposed mode-dependent H∞ filter, the systems have the stochastic stability and better ability of restraining disturbances stochastically, and the given prescribed H∞ performance is guaranteed. Simulation results illustrate the effectiveness of developed techniques.
基金the National Natural Science Fund of China(61471080)Training Plan for Young Backbone Teachers in Colleges and Universities of Henan Province(2018GGJS171).
文摘To solve the problem of data fusion for prior information such as track information and train status in train positioning,an adaptive H∞filtering algorithm with combination constraint is proposed,which fuses prior information with other sensor information in the form of constraints.Firstly,the train precise track constraint method of the train is proposed,and the plane position constraint and train motion state constraints are analysed.A model for combining prior information with constraints is established.Then an adaptive H∞filter with combination constraints is derived based on the adaptive adjustment method of the robustness factor.Finally,the positioning effect of the proposed algorithm is simulated and analysed under the conditions of a straight track and a curved track.The results show that the positioning accuracy of the algorithm with constrained filtering is significantly better than that of the algorithm without constrained filtering and that the algorithm with constrained filtering can achieve better performance when combined with track and condition information,which can significantly reduce the train positioning error.The effectiveness of the proposed algorithm is verified.
基金supported in part by the National Natural Science Foundation of China(62103004,62273088,62273005,62003121)Anhui Provincial Natural Science Foundation of China(2108085QA13)+4 种基金the Natural Science Foundation of Zhejiang Province(LY24F030006)the Science and Technology Plan of Wuhu City(2022jc24)Anhui Polytechnic University Youth Top-Notch Talent Support Program(2018BJRC009)Anhui Polytechnic University High-End Equipment Intelligent Control Innovation Team(2021CXTD005)Anhui Future Technology Research Institute Foundation(2023qyhz08,2023qyhz09)。
文摘In this paper,the recursive filtering problem is considered for stochastic systems over filter-and-forward successive relay(FFSR)networks.An FFSR is located between the sensor and the remote filter to forward the measurement.In the successive relay,two cooperative relay nodes are adopted to forward the signals alternatively,thereby existing switching characteristics and inter-relay interferences(IRI).Since the filter-and-forward scheme is employed,the signal received by the relay is retransmitted after it passes through a linear filter.The objective of the paper is to concurrently design optimal recursive filters for FFSR and stochastic systems against switching characteristics and IRI of relays.First,a uniform measurement model is proposed by analyzing the transmission mechanism of FFSR.Then,novel filter structures with switching parameters are constructed for both FFSR and stochastic systems.With the help of the inductive method,filtering error covariances are presented in the form of coupled difference equations.Next,the desired filter gain matrices are further obtained by minimizing the trace of filtering error covariances.Moreover,the stability performance of the filtering algorithm is analyzed where the uniform bound is guaranteed on the filtering error covariance.Finally,the effectiveness of the proposed filtering method over FFSR is verified by a three-order resistance-inductance-capacitance circuit system.
基金supported in part by the National Key R&D Program of China(2022YFC3401303)the Natural Science Foundation of Jiangsu Province(BK20211528)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KFCX22_2300)。
文摘In the era of exponential growth of data availability,the architecture of systems has a trend toward high dimensionality,and directly exploiting holistic information for state inference is not always computationally affordable.This paper proposes a novel Bayesian filtering algorithm that considers algorithmic computational cost and estimation accuracy for high-dimensional linear systems.The high-dimensional state vector is divided into several blocks to save computation resources by avoiding the calculation of error covariance with immense dimensions.After that,two sequential states are estimated simultaneously by introducing an auxiliary variable in the new probability space,mitigating the performance degradation caused by state segmentation.Moreover,the computational cost and error covariance of the proposed algorithm are analyzed analytically to show its distinct features compared with several existing methods.Simulation results illustrate that the proposed Bayesian filtering can maintain a higher estimation accuracy with reasonable computational cost when applied to high-dimensional linear systems.
基金supported in part by the National Natural Science Foundation of China(61933007,U21A2019,U22A2044,61973102,62073180)the Natural Science Foundation of Shandong Province of China(ZR2021MF088)+1 种基金the Hainan Province Science and Technology Special Fund of China(ZDYF2022SHFZ105)the Royal Society of the UK,and the Alexander vonHumboldt Foundation of Germany。
文摘This paper focuses on the quadratic nonfragile filtering problem for linear non-Gaussian systems under multiplicative noises,multiple missing measurements as well as the dynamic event-triggered transmission scheme.The multiple missing measurements are characterized through random variables that obey some given probability distributions,and thresholds of the dynamic event-triggered scheme can be adjusted dynamically via an auxiliary variable.Our attention is concentrated on designing a dynamic event-triggered quadratic nonfragile filter in the well-known minimum-variance sense.To this end,the original system is first augmented by stacking its state/measurement vectors together with second-order Kronecker powers,thus the original design issue is reformulated as that of the augmented system.Subsequently,we analyze statistical properties of augmented noises as well as high-order moments of certain random parameters.With the aid of two well-defined matrix difference equations,we not only obtain upper bounds on filtering error covariances,but also minimize those bounds via carefully designing gain parameters.Finally,an example is presented to explain the effectiveness of this newly established quadratic filtering algorithm.
基金supported by the National Key Research and Development Program of China (No. 2022YFB1902700)the National Natural Science Foundation of China (No. 11875129)+3 种基金the Fund of the State Key Laboratory of Intense Pulsed Radiation Simulation and Effect (No. SKLIPR1810)Fund of Innovation Center of Radiation Application (No. KFZC2020020402)Fund of the State Key Laboratory of Nuclear Physics and Technology,Peking University (No. NPT2020KFY08)the Joint Innovation Fund of China National Uranium Co.,Ltd.,State Key Laboratory of Nuclear Resources and Environment,East China University of Technology (No. 2022NRE-LH-02)。
文摘The most critical part of a neutron computed tomography(NCT) system is the image processing algorithm,which directly affects the quality and speed of the reconstructed images.Various types of noise in the system can degrade the quality of the reconstructed images.Therefore,to improve the quality of the reconstructed images of NCT systems,efficient image processing algorithms must be used.The anisotropic diffusion filtering(ADF) algorithm can not only effectively suppress the noise in the projection data,but also preserve the image edge structure information by reducing the diffusion at the image edges.Therefore,we propose the application of the ADF algorithm for NCT image reconstruction.To compare the performance of different algorithms in NCT systems,we reconstructed images using the ordered subset simultaneous algebraic reconstruction technique(OS-SART) algorithm with different regular terms as image processing algorithms.In the iterative reconstruction,we selected two image processing algorithms,the Total Variation and split Bregman solved total variation algorithms,for comparison with the performance of the ADF algorithm.Additionally,the filtered back-projection algorithm was used for comparison with an iterative algorithm.By reconstructing the projection data of the numerical and clock models,we compared and analyzed the effects of each algorithm applied in the NCT system.Based on the reconstruction results,OS-SART-ADF outperformed the other algorithms in terms of denoising,preserving the edge structure,and suppressing artifacts.For example,when the 3D Shepp–Logan was reconstructed at 25 views,the root mean square error of OS-SART-ADF was the smallest among the four iterative algorithms,at only 0.0292.The universal quality index,mean structural similarity,and correlation coefficient of the reconstructed image were the largest among all algorithms,with values of 0.9877,0.9878,and 0.9887,respectively.
基金Project supported by the National Natural Science Foundation of China (Grant Nos.12174077 and 12174051)the Science Foundation of GuangDong Province (Grant No.2021A1515012363)GuangDong Basic and Applied Basic Research Foundation (Grant No.2022A1515110011)。
文摘We investigated the electric controllable spin-filtering effect in a zigzag phosphorene nanoribbon(ZPNR) based normal–antiferromagnet–normal junction. Two ferromagnets are closely coupled to the edges of the nanoribbon and form the edge-to-edge antiferromagnetism. Under an in-plane electric field, the two degenerate edge bands of the edge-to-edge antiferromagnet split into four spin-polarized sub-bands and a 100% spin-polarized current can be easily induced with the maximal conductance 2e~2/h. The spin polarization changes with the strength of the electric field and the exchange field,and changes sign at opposite electric fields. The spin-polarized current switches from one edge to the other by reversing the direction of the electric field. The edge current can also be controlled spatially by changing the electric potential of the scattering region. The manipulation of edge current is useful in spin-transfer-torque magnetic random-access memory and provides a practical way to develop controllable spintronic devices.
文摘Passive detection of low-slow-small(LSS)targets is easily interfered by direct signal and multipath clutter,and the traditional clutter suppression method has the contradiction between step size and convergence rate.In this paper,a frequency domain clutter suppression algorithm based on sparse adaptive filtering is proposed.The pulse compression operation between the error signal and the input reference signal is added to the cost function as a sparsity constraint,and the criterion for filter weight updating is improved to obtain a purer echo signal.At the same time,the step size and penalty factor are brought into the adaptive iteration process,and the input data is used to drive the adaptive changes of parameters such as step size.The proposed algorithm has a small amount of calculation,which improves the robustness to parameters such as step size,reduces the weight error of the filter and has a good clutter suppression performance.