In the hierarchical random effect linear model, the Bayes estimator of random parameter are not only dependent on specific prior distribution but also it is difficult to calculate in most cases. This paper derives the...In the hierarchical random effect linear model, the Bayes estimator of random parameter are not only dependent on specific prior distribution but also it is difficult to calculate in most cases. This paper derives the distributed-free optimal linear estimator of random parameters in the model by means of the credibility theory method. The estimators the authors derive can be applied in more extensive practical scenarios since they are only dependent on the first two moments of prior parameter rather than on specific prior distribution. Finally, the results are compared with some classical models and a numerical example is given to show the effectiveness of the estimators.展开更多
The problem of fault estimation is investigated for a class of uncertain switched systems with time-varying delay. A robust observer-based fault estimator is designed such that the augment error system is exponentiall...The problem of fault estimation is investigated for a class of uncertain switched systems with time-varying delay. A robust observer-based fault estimator is designed such that the augment error system is exponentially stable and the H∞ performance index meets the predefined requirements. Based on the multiple Lyapunov-Krasovskii functions and the average dwell-time method, the delay dependent sufficient conditions on the existence of desired fault estimator are established. However, since these conditions are not linear matrix inequalities(LMIS), they can not be solved by MATLAB. By using a novel method, these conditions are presented in terms of LMIS. Finally, a numerical example is carried out. The designed fault estimator could tract the fault signal timely. Besides, the error between estimation and fault is very small. Therefore, the validity of the obtained results is illustrated.展开更多
In target tracking applications,the Doppler measurement contains information of the target range rate,which has the potential capability to improve the tracking performance.However,the nonlinear degree between the mea...In target tracking applications,the Doppler measurement contains information of the target range rate,which has the potential capability to improve the tracking performance.However,the nonlinear degree between the measurement and the target state increases with the introduction of the Doppler measurement.Therefore,target tracking in the Doppler radar is a nonlinear filtering problem.In order to handle this problem,the Kalman filter form of best linear unbiased estimation(BLUE)with position measurements is proposed,which is combined with the sequential filtering algorithm to handle the Doppler measurement further,where the statistic characteristic of the converted measurement error is calculated based on the predicted information in the sequential filter.Moreover,the algorithm is extended to the maneuvering target tracking case,where the interacting multiple model(IMM)algorithm is used as the basic framework and the model probabilities are updated according to the BLUE position filter and the sequential filter,and the final estimation is a weighted sum of the outputs from the sequential filters and the model probabilities.Simulation results show that compared with existing approaches,the proposed algorithm can realize target tracking with preferable tracking precision and the extended method can achieve effective maneuvering target tracking.展开更多
The mismatch effect induced by the radial motion of a target is analyzed for linear frequency modulated （LFM） signals. Then, a novel integrated processing scheme is proposed to re- solve the delay-Doppler coupling e...The mismatch effect induced by the radial motion of a target is analyzed for linear frequency modulated （LFM） signals. Then, a novel integrated processing scheme is proposed to re- solve the delay-Doppler coupling effect in LFM pulse compression. Therefore the range and radial velocity of the target can be si- multaneously estimated with a narrowband LFM pulse. Finally, numerical simulation results demonstrate the effectiveness and good performance of the proposed method.展开更多
In this paper,parameter estimation of linear frequency modulation(LFM)signals containing additive white Gaussian noise is studied.Because the center frequency estimation of an LFM signal is affected by the error propa...In this paper,parameter estimation of linear frequency modulation(LFM)signals containing additive white Gaussian noise is studied.Because the center frequency estimation of an LFM signal is affected by the error propagation effect,resulting in a higher signal to noise ratio(SNR)threshold,a parameter estimation method for LFM signals based on time reversal is proposed.The proposed method avoids SNR loss in the process of estimating the frequency,thus reducing the SNR threshold.The simulation results show that the threshold is reduced by 5 dB compared with the discrete polynomial transform(DPT)method,and the root-mean-square error(RMSE)of the proposed estimator is close to the Cramer-Rao lower bound(CRLB).展开更多
This paper proposes a robust method of parameter estimation and data classification for multiple-structural data based on the linear error in variable（EIV） model.The traditional EIV model fitting problem is analyzed...This paper proposes a robust method of parameter estimation and data classification for multiple-structural data based on the linear error in variable（EIV） model.The traditional EIV model fitting problem is analyzed and a robust growing algorithm is developed to extract the underlying linear structure of the observed data.Under the structural density assumption,the C-step technique borrowed from the Rousseeuw＇s robust MCD estimator is used to keep the algorithm robust and the mean-shift algorithm is adopted to ensure a good initialization.To eliminate the model ambiguities of the multiple-structural data,statistical hypotheses tests are used to refine the data classification and improve the accuracy of the model parameter estimation.Experiments show that the efficiency and robustness of the proposed algorithm.展开更多
The problem of state estimation for uncertain systems has attracted a recurring interest in the past decade. In this paper, we shall give an overview on some of the recent development in the area by focusing on the ro...The problem of state estimation for uncertain systems has attracted a recurring interest in the past decade. In this paper, we shall give an overview on some of the recent development in the area by focusing on the robust H2 (Kaiman) filtering of uncertain discrete-time systems. The robust H2 estimation is concerned with the design of a fixed estimator for a family of plants under consideration such that the estimation error covariance is of a minimal upper bound. The uncertainty under consideration includes norm-bounded uncertainty and polytopic uncertainty. In the finite horizon case, we shall discuss a parameterized difference Riccati equation approach for systems with normbounded uncertainty and pinpoint the difference of state estimation between systems without uncertainty and those with uncertainty. In the infinite horizon case, we shall deal with both the norm-bounded and polytopic uncertainties using a linear matrix inequality (LMI) approach. In particular, we shall demonstrate how the conservatism of design can be improved using a slack variable technique. We also propose an iterative algorithm to refine a designed estimator. An example will be given to compare estimators designed using various techniques.展开更多
The design problem of non-fragile estimator is addressed for a class of perturbed linear continuous systems. The perturbations occur on the plant and estimator parameters. The estimator designed should force the error...The design problem of non-fragile estimator is addressed for a class of perturbed linear continuous systems. The perturbations occur on the plant and estimator parameters. The estimator designed should force the error system to achieve the desired decay rate and force the steady error variance less than the specified upper bound irrelevancy of the admissible plant perturbations and estimator perturbations. Consistency problem of the decay rate with the variance upper bound is first considered via linear matrix inequality (LMI) approach. The solution of the estimator parameter under specifications to be consistent is then discussed. The consistency condition of specifications and estimator parameter solution are transformed to feasible or minimum problems subject to a set of LMI respectively. The method is illustrated by a numerical example.展开更多
基金supported by the National Science Foundation of China under Grant Nos.71361015,71340010,71371074the Jiangxi Provincial Natural Science Foundation under Grant No.20142BAB201013+2 种基金China Postdoctoral Science Foundation under Grant No.2013M540534China Postdoctoral Fund special Project under Grant No.2014T70615Jiangxi Postdoctoral Science Foundation under Grant No.2013KY53
文摘In the hierarchical random effect linear model, the Bayes estimator of random parameter are not only dependent on specific prior distribution but also it is difficult to calculate in most cases. This paper derives the distributed-free optimal linear estimator of random parameters in the model by means of the credibility theory method. The estimators the authors derive can be applied in more extensive practical scenarios since they are only dependent on the first two moments of prior parameter rather than on specific prior distribution. Finally, the results are compared with some classical models and a numerical example is given to show the effectiveness of the estimators.
基金Project(61273158)supported by the National Natural Science Foundation of China
文摘The problem of fault estimation is investigated for a class of uncertain switched systems with time-varying delay. A robust observer-based fault estimator is designed such that the augment error system is exponentially stable and the H∞ performance index meets the predefined requirements. Based on the multiple Lyapunov-Krasovskii functions and the average dwell-time method, the delay dependent sufficient conditions on the existence of desired fault estimator are established. However, since these conditions are not linear matrix inequalities(LMIS), they can not be solved by MATLAB. By using a novel method, these conditions are presented in terms of LMIS. Finally, a numerical example is carried out. The designed fault estimator could tract the fault signal timely. Besides, the error between estimation and fault is very small. Therefore, the validity of the obtained results is illustrated.
基金This work was supported by the Basic Research Operation Foundation for Central University(ZYGX2016J039).
文摘In target tracking applications,the Doppler measurement contains information of the target range rate,which has the potential capability to improve the tracking performance.However,the nonlinear degree between the measurement and the target state increases with the introduction of the Doppler measurement.Therefore,target tracking in the Doppler radar is a nonlinear filtering problem.In order to handle this problem,the Kalman filter form of best linear unbiased estimation(BLUE)with position measurements is proposed,which is combined with the sequential filtering algorithm to handle the Doppler measurement further,where the statistic characteristic of the converted measurement error is calculated based on the predicted information in the sequential filter.Moreover,the algorithm is extended to the maneuvering target tracking case,where the interacting multiple model(IMM)algorithm is used as the basic framework and the model probabilities are updated according to the BLUE position filter and the sequential filter,and the final estimation is a weighted sum of the outputs from the sequential filters and the model probabilities.Simulation results show that compared with existing approaches,the proposed algorithm can realize target tracking with preferable tracking precision and the extended method can achieve effective maneuvering target tracking.
基金supported by the Pre-Research Foundation of National Defence of China
文摘The mismatch effect induced by the radial motion of a target is analyzed for linear frequency modulated （LFM） signals. Then, a novel integrated processing scheme is proposed to re- solve the delay-Doppler coupling effect in LFM pulse compression. Therefore the range and radial velocity of the target can be si- multaneously estimated with a narrowband LFM pulse. Finally, numerical simulation results demonstrate the effectiveness and good performance of the proposed method.
基金supported by the Regional Joint Fund for Basic and Applied Basic Research of Guangdong Province(2019B1515120009)the Defense Basic Scientific Research Program(61424132005).
文摘In this paper,parameter estimation of linear frequency modulation(LFM)signals containing additive white Gaussian noise is studied.Because the center frequency estimation of an LFM signal is affected by the error propagation effect,resulting in a higher signal to noise ratio(SNR)threshold,a parameter estimation method for LFM signals based on time reversal is proposed.The proposed method avoids SNR loss in the process of estimating the frequency,thus reducing the SNR threshold.The simulation results show that the threshold is reduced by 5 dB compared with the discrete polynomial transform(DPT)method,and the root-mean-square error(RMSE)of the proposed estimator is close to the Cramer-Rao lower bound(CRLB).
基金Supported by National Natural Science Foundation of China （60574083, 60811120024）, Graduate Innovation Research Foundation of Jiangsu Province （CX08B-090Z）, and Doctoral Innovation Foundation of Nanjing University of Aeronautics and Astronautics （BCXJ08-03）
基金Supported by National Basic Research and Development Program of China （973 Program） （2009CB320600）, National Natural Science Foundation of China （60774004）, Taishan Scholar Construction Engineering of Shandong Government, National Natural Science Foundation for Distinguished Young Scholars of China （60825304）
基金Supported by National Natural Science Foundation of China （10571036） the Key Discipline Development Program of Beijing Municipal Commission （XK100080537）
基金supported by the National High Technology Research and Development Program of China (863 Program) (2007AA04Z227)
文摘This paper proposes a robust method of parameter estimation and data classification for multiple-structural data based on the linear error in variable（EIV） model.The traditional EIV model fitting problem is analyzed and a robust growing algorithm is developed to extract the underlying linear structure of the observed data.Under the structural density assumption,the C-step technique borrowed from the Rousseeuw＇s robust MCD estimator is used to keep the algorithm robust and the mean-shift algorithm is adopted to ensure a good initialization.To eliminate the model ambiguities of the multiple-structural data,statistical hypotheses tests are used to refine the data classification and improve the accuracy of the model parameter estimation.Experiments show that the efficiency and robustness of the proposed algorithm.
文摘The problem of state estimation for uncertain systems has attracted a recurring interest in the past decade. In this paper, we shall give an overview on some of the recent development in the area by focusing on the robust H2 (Kaiman) filtering of uncertain discrete-time systems. The robust H2 estimation is concerned with the design of a fixed estimator for a family of plants under consideration such that the estimation error covariance is of a minimal upper bound. The uncertainty under consideration includes norm-bounded uncertainty and polytopic uncertainty. In the finite horizon case, we shall discuss a parameterized difference Riccati equation approach for systems with normbounded uncertainty and pinpoint the difference of state estimation between systems without uncertainty and those with uncertainty. In the infinite horizon case, we shall deal with both the norm-bounded and polytopic uncertainties using a linear matrix inequality (LMI) approach. In particular, we shall demonstrate how the conservatism of design can be improved using a slack variable technique. We also propose an iterative algorithm to refine a designed estimator. An example will be given to compare estimators designed using various techniques.
文摘The design problem of non-fragile estimator is addressed for a class of perturbed linear continuous systems. The perturbations occur on the plant and estimator parameters. The estimator designed should force the error system to achieve the desired decay rate and force the steady error variance less than the specified upper bound irrelevancy of the admissible plant perturbations and estimator perturbations. Consistency problem of the decay rate with the variance upper bound is first considered via linear matrix inequality (LMI) approach. The solution of the estimator parameter under specifications to be consistent is then discussed. The consistency condition of specifications and estimator parameter solution are transformed to feasible or minimum problems subject to a set of LMI respectively. The method is illustrated by a numerical example.
