The Experts below are selected from a list of 2403 Experts worldwide ranked by ideXlab platform
Shuli Sun - One of the best experts on this subject based on the ideXlab platform.
-
distributed fusion filter for multi sensor systems with finite step Correlated Noises
Information Fusion, 2019Co-Authors: Tian Tian, Shuli Sun, Honglei LinAbstract:Abstract This paper addresses the distributed fusion filtering problem for multi-sensor systems with finite-step Correlated Noises. The process noise and observation Noises of different sensors are finite-step auto- and cross-Correlated, respectively. Based on the optimal local filtering algorithms that we presented before, the filtering error cross-covariance matrices between any two local filters are derived based on an innovation analysis approach. A distributed fusion filter is put forward by using matrix-weighted fusion estimation algorithm in the linear unbiased minimum variance sense. Finally, the proposed algorithms are extended to systems with random parameter matrices. Two simulation examples are given to show the effectiveness of the proposed algorithms.
-
optimal sequential fusion estimation with stochastic parameter perturbations fading measurements and Correlated Noises
IEEE Transactions on Signal Processing, 2018Co-Authors: Honglei Lin, Shuli SunAbstract:This paper focuses on the linear optimal recursive sequential fusion filter design for multisensor systems subject to stochastic parameter perturbations, fading measurements, and Correlated Noises. The stochastic parameter perturbations existing in the state model are described by white multiplicative Noises. The fading measurement phenomena for different sensors are described by independent random variables with known statistical properties. Moreover, the measurement Noises of different sensors are Correlated with each other and also Correlated with the system noise at the same time step. First, a model equivalent to the original system is established by transferring the multiplicative Noises into the additive Noises. Then, based on the equivalent model and an innovation analysis method, a sequential fusion filter in the linear minimum variance sense is proposed to solve the linear optimal state estimation problem in real time according to the arriving order of measurements from different sensors. Finally, the equivalence on estimation accuracy of the proposed sequential fusion filter and the centralized fusion filter is strictly proven, which shows the optimality of the proposed sequential fusion algorithm. Moreover, the proposed sequential fusion filter has a reduced computational burden. Compared with the distributed matrix-weighted fusion filter, the computation of cross-covariance matrices is avoided and the estimation accuracy is improved. Finally, a simulation example verifies the effectiveness of the proposed sequential fusion filtering algorithm.
-
Distributed Fusion Estimator for Multisensor Multirate Systems With Correlated Noises
IEEE Transactions on Systems Man and Cybernetics: Systems, 2018Co-Authors: Honglei Lin, Shuli SunAbstract:A distributed fusion estimation algorithm is studied for multisensor multirate systems with Correlated Noises, where the state update rate is positive integer multiples of the measurement sampling rates and different sensors sample uniformly with different sampling rates. The measurement Noises from different sensors are Correlated with each other and are also Correlated with the process noise. First, the state space model is established at the measurement sampling points (MSPs). Then, the optimal local filter at the MSPs and optimal local estimators (LEs) at the state update points are presented by an innovation analysis approach, respectively. Moreover, the cross-covariance matrices of estimation errors between any two LEs are derived, which involves three jointly recursive difference equations. At last, a distributed fusion estimator is proposed by applying the matrix-weighted fusion estimation algorithm in the linear minimum variance sense. The stability of the proposed algorithms is analyzed. Simulation results illustrate the effectiveness of the algorithms.
-
state estimators for systems with random parameter matrices stochastic nonlinearities fading measurements and Correlated Noises
Information Sciences, 2017Co-Authors: Shuli Sun, Tian Tian, Lin HongleiAbstract:Using the innovation analysis approach, the optimal linear state estimators, including the filter, predictor and smoother, in the linear minimum variance (LMV) sense are presented for a class of nonlinear discrete-time stochastic uncertain systems with fading measurements and Correlated Noises. Stochastic uncertainties of parameter matrices are depicted by Correlated multiplicative Noises. Stochastic nonlinearities are characterized by a known conditional mean and covariance. Different sensor channels have different fading measurement rates. The process and measurement Noises are finite-step auto- and/or cross-Correlated with each other. Two simulation examples verify the effectiveness of the proposed algorithms.
-
optimal recursive estimation for networked descriptor systems with packet dropouts multiplicative Noises and Correlated Noises
Aerospace Science and Technology, 2017Co-Authors: Xin Wang, Shuli SunAbstract:Abstract This paper addresses the optimal linear estimation problem for a class of networked descriptor systems with multiple packet dropouts, measurement multiplicative Noises and finite-step Correlated process and measurement Noises. Based on a fast–slow subsystem decomposition approach (FSD), the descriptor system is transformed into two reduced-order linear nonsingular subsystems with finite-step Correlated Noises. Optimal linear estimators including filter, predictor and smoother with corresponding estimation error covariance matrices for the states and Noises of new systems are developed via the innovation analysis approach. Then, the optimal linear estimators are obtained for the original descriptor system. An example shows the effectiveness of the proposed algorithms.
Honglei Lin - One of the best experts on this subject based on the ideXlab platform.
-
distributed fusion filter for multi sensor systems with finite step Correlated Noises
Information Fusion, 2019Co-Authors: Tian Tian, Shuli Sun, Honglei LinAbstract:Abstract This paper addresses the distributed fusion filtering problem for multi-sensor systems with finite-step Correlated Noises. The process noise and observation Noises of different sensors are finite-step auto- and cross-Correlated, respectively. Based on the optimal local filtering algorithms that we presented before, the filtering error cross-covariance matrices between any two local filters are derived based on an innovation analysis approach. A distributed fusion filter is put forward by using matrix-weighted fusion estimation algorithm in the linear unbiased minimum variance sense. Finally, the proposed algorithms are extended to systems with random parameter matrices. Two simulation examples are given to show the effectiveness of the proposed algorithms.
-
globally optimal sequential and distributed fusion state estimation for multi sensor systems with cross Correlated Noises
Automatica, 2019Co-Authors: Honglei Lin, Shulin SunAbstract:This paper is concerned with globally optimal sequential and distributed fusion estimation algorithms in the linear minimum variance (LMV) sense for multi-sensor systems with cross-Correlated Noises, where the measurement Noises from different sensors are cross-Correlated with each other at the same time step and Correlated with the system noise at the previous time step. First, a globally optimal sequential fusion filter is proposed by considering the estimators for measurement Noises. The equivalence on estimation accuracy of the proposed sequential fusion filter and centralized fusion filter is proven. It has reduced computational cost since it avoids the measurement augmentation. Then, a distributed fusion filter is also proposed by considering the prior fusion estimator and feedback from the fusion center. Under the condition that local gain matrices are full column rank, the proposed distributed fusion filter has the same estimation accuracy as the centralized fusion filter, that is, it also has global optimality. Their equivalence on estimation accuracy is proven. Stability and steady-state properties of the proposed algorithms are analyzed. A sufficient condition for the existence of steady-state filters is given. Finally, simulation results for a target tracking system show the effectiveness of the proposed algorithms.
-
optimal sequential fusion estimation with stochastic parameter perturbations fading measurements and Correlated Noises
IEEE Transactions on Signal Processing, 2018Co-Authors: Honglei Lin, Shuli SunAbstract:This paper focuses on the linear optimal recursive sequential fusion filter design for multisensor systems subject to stochastic parameter perturbations, fading measurements, and Correlated Noises. The stochastic parameter perturbations existing in the state model are described by white multiplicative Noises. The fading measurement phenomena for different sensors are described by independent random variables with known statistical properties. Moreover, the measurement Noises of different sensors are Correlated with each other and also Correlated with the system noise at the same time step. First, a model equivalent to the original system is established by transferring the multiplicative Noises into the additive Noises. Then, based on the equivalent model and an innovation analysis method, a sequential fusion filter in the linear minimum variance sense is proposed to solve the linear optimal state estimation problem in real time according to the arriving order of measurements from different sensors. Finally, the equivalence on estimation accuracy of the proposed sequential fusion filter and the centralized fusion filter is strictly proven, which shows the optimality of the proposed sequential fusion algorithm. Moreover, the proposed sequential fusion filter has a reduced computational burden. Compared with the distributed matrix-weighted fusion filter, the computation of cross-covariance matrices is avoided and the estimation accuracy is improved. Finally, a simulation example verifies the effectiveness of the proposed sequential fusion filtering algorithm.
-
Distributed Fusion Estimator for Multisensor Multirate Systems With Correlated Noises
IEEE Transactions on Systems Man and Cybernetics: Systems, 2018Co-Authors: Honglei Lin, Shuli SunAbstract:A distributed fusion estimation algorithm is studied for multisensor multirate systems with Correlated Noises, where the state update rate is positive integer multiples of the measurement sampling rates and different sensors sample uniformly with different sampling rates. The measurement Noises from different sensors are Correlated with each other and are also Correlated with the process noise. First, the state space model is established at the measurement sampling points (MSPs). Then, the optimal local filter at the MSPs and optimal local estimators (LEs) at the state update points are presented by an innovation analysis approach, respectively. Moreover, the cross-covariance matrices of estimation errors between any two LEs are derived, which involves three jointly recursive difference equations. At last, a distributed fusion estimator is proposed by applying the matrix-weighted fusion estimation algorithm in the linear minimum variance sense. The stability of the proposed algorithms is analyzed. Simulation results illustrate the effectiveness of the algorithms.
-
distributed fusion estimation for multi sensor asynchronous sampling systems with Correlated Noises
International Journal of Systems Science, 2017Co-Authors: Honglei Lin, Shuli SunAbstract:This paper is concerned with the distributed fusion estimation problem for a class of multi-sensor asynchronous sampling systems with Correlated Noises. The state updates uniformly and the sensors sample randomly. Based on the measurement augmentation method, the asynchronous sampling system is transformed to the synchronous sampling one. Local filter is designed by using an innovation analysis approach. Then, the filtering error cross-covariance matrix between any two local filters is derived. Finally, the optimal distributed fusion filter is proposed by using matrix-weighted fusion algorithm in the linear minimum variance sense. Simulation results show the effectiveness of the proposed algorithms.
Li Cao - One of the best experts on this subject based on the ideXlab platform.
-
effect of Correlated Noises in an optical bistable system
Physical Review A, 2008Co-Authors: Li Zhang, Li CaoAbstract:In this paper we focus on the state equation of absorptive optical bistable system with cross-Correlated multiplicative noise $\ensuremath{\xi}(t)$ and additive noise $\ensuremath{\eta}(t)$. We find that the area of optical bistability with positive cross-correlation expands in comparison with the deterministic case. Furthermore, a very peculiar phenomenon is shown, namely when the cooperativity parameter $Cl4$, noise still can induce optical bistability. The phenomenon of noise-induced optical bistability in this paper is just the example of noise-induced phase transition.
-
Intensity Correlation Function and Relaxation Time of a Single-Mode Laser Driven by Cross-Correlated Noises for the Case of Nonzero Correlation Time
Physica Scripta, 2004Co-Authors: Chong-wei Xie, D. C. Mei, Li CaoAbstract:We consider a single-mode laser model driven by Correlated quantum and pump noise and study the effects of the cross correlation time τ between Noises on the steady-state intensity correlation function and the associated relaxation time. Based on an approximated Fokker–Planck description of the laser model and the Stratonovich-like ansatz, we find the steady-state probability distribution function Pst(I), the steady-state intensity correlation function K(s) and the associated relaxation time T. From numerical computations we found the following: (1) τ suppresses the fluctuation of the laser intensity for the case of positively Correlated Noises (i.e. the correlation strength between Noises λ > 0) and increases the intensity fluctuation for the case of negatively Correlated Noises (λ > 0); (2) τ slows down the decay of the intensity correlation for the case of λ > 0 but speeds up the decay for the case of λ 0.
-
transient properties of a bistable system driven by cross Correlated Noises correlation times are nonzero case
Chinese Physics Letters, 1999Co-Authors: Guangzhong Xie, Dongcheng Mei, Li CaoAbstract:The transient properties of a bistable system driven by cross-Correlated Noises are investigated, the correlation times of the correlations between the Noises are nonzero. The explicit expression of the mean first-passage time (MFPT) is obtained. From numerical computations we find the following.(1) The MPPT of the system is affected by the correlation time tau and the correlation strength lambda, tau and lambda play opposing roles in the MFPT. (2) For the case of perfectly Correlated Noises (lambda = 1), the MFPT corresponding to alpha > D and alpha < D (alpha and D are the additive noise and multiplicative noise intensities, respectively) exhibit the same behaviors and the MFPT for alpha = D is continuous, which is very different from the case of tau = 0 [Phys. Rev. E 53 (1996) 5764].
-
mean first passage time of a bistable kinetic model driven by cross Correlated Noises
Physical Review E, 1999Co-Authors: Guangzhong Xie, Dongcheng Mei, Li CaoAbstract:The transient properties of a bistable system driven by cross-Correlated Noises are investigated; the correlation times of the correlations between the Noises are nonzero. The mean first-passage time (MFPT) is calculated. From numerical computations we find the following: (1) The MFPT of the system is affected by both the correlation time \ensuremath{\tau} and the correlation strength \ensuremath{\lambda}; (2) \ensuremath{\tau} and \ensuremath{\lambda} play opposing roles in the MFPT; (3) when \ensuremath{\lambda} or $\ensuremath{\alpha}/D(\ensuremath{\alpha}$ and D are the additive and multiplicative noise intensities respectively) are far away from 1, the MFPT as a function of \ensuremath{\tau} is monotonic; however, when both $\ensuremath{\alpha}/D$ and \ensuremath{\lambda} approach 1, the MFPT as a function of \ensuremath{\tau} is nonmonotonic; (4) for the case of perfectly Correlated Noises (\ensuremath{\lambda}=1), the MFPT corresponding to $\ensuremath{\alpha}gD$ and $\ensuremath{\alpha}lD$ exhibit the same behaviors and the MFPT for $\ensuremath{\alpha}=D$ is continuous, which is very different from the case of the $\ensuremath{\tau}=0$ [Phys. Rev. E 53, 5764 (1996)].
-
stochastic dynamics for systems driven by Correlated Noises
Physics Letters A, 1994Co-Authors: Li CaoAbstract:Abstract Recently, Fulinski and Telejko [Phys. Lett. A 152 (1991) 11] have studied the effect of correlation of additive and multiplicative Noises. However, they have not obtained a general Fokker-Planck equation (GFPE) which can describe the dynamics of the system driven by multi-Noises with an arbitrary degree of correlation. In this Letter, we have derived a GFPE for a one-dimensional system from the Langevin equation (LE) with multi-Noises and an arbitrary degree of correlation between these Noises. Using this GFPE, we study the single bistable kinetic process driven by correlative additive and multiplicative Noises. In conclusion, the results obtained in this Letter provide a correct foundation for the treatment of the effect of correlation of the Noises.
Shuguang Cui - One of the best experts on this subject based on the ideXlab platform.
-
study of gaussian relay channels with Correlated Noises
IEEE Transactions on Communications, 2011Co-Authors: Lili Zhang, Jinhua Jiang, Andrea Goldsmith, Shuguang CuiAbstract:In this paper, we consider the full-duplex and half-duplex Gaussian relay channels where the Noises at the relay and destination are arbitrarily Correlated. We first derive the capacity upper bound and the achievable rates with three existing schemes: Decode-and-Forward (DF), Compress-and-Forward (CF), and Amplify-and-Forward (AF). We present two capacity results under specific noise correlation coefficients, one being achieved by DF and the other being achieved by direct link transmission (or a special case of CF). The channel for the former capacity result is equivalent to the traditional Gaussian degraded relay channel and the latter corresponds to the Gaussian reversely-degraded relay channel. For CF and AF schemes, we show that their achievable rates are strictly decreasing functions of the correlation coefficient when the correlation coefficient is negative. Moreover, when the noise correlation coefficient is positive, the CF achievable rate may also outperform the independent-noise case if the noise correlation coefficient is within a certain range. Through numerical comparisons under different channel settings, we observe that although DF completely disregards the noise correlation while the other two can potentially exploit such extra information, none of the three relay schemes always outperforms the others over different correlation coefficients. Moreover, the exploitation of noise correlation by CF and AF accrues more benefit when the source-relay link is weak. This paper also considers the optimal power allocation problem under the Correlated-noise channel setting. With individual power constraints at the relay and the source, it is shown that the relay should use all its available power to maximize the achievable rates under any correlation coefficient. With a total power constraint across the source and the relay, the achievable rates are proved to be concave functions over the power allocation factor for AF and CF under full-duplex mode, where the closed-form power allocation strategy is derived.
-
study of half duplex gaussian relay channels with Correlated Noises
Wireless Communications and Networking Conference, 2010Co-Authors: Lili Zhang, Jinhua Jiang, Shuguang CuiAbstract:In this paper, we consider a half-duplex Gaussian relay channel where the Noises at the relay and destination are arbitrarily Correlated. For this generalized relay channel, we first evaluate the cut-set bound as well as the achievable rates with three existing relay schemes: Decode-and-Forward (DF), Compress-and-Forward (CF), and Amplify-and-Forward (AF), with performance comparison under various channel settings. We observe that although DF completely disregards the noise correlation while the other two could exploit such extra information, none of the three relay schemes always outperforms the others over different correlation coefficients; however, the exploitation of noise correlation by CF and AF leads to more significant benefit when the source-relay channel is weak. It is further shown that a negative noise correlation is always helpful for AF. We also establish two capacity-achieving results under two special noise correlation coefficients, with one being achieved by DF and the other being achieved by direct link transmission (or a special case of CF), which correspond to the capacity results for the traditional Gaussian degraded relay channel and the Gaussian reversely-degraded one.
-
achievable rates and capacity for gaussian relay channels with Correlated Noises
International Symposium on Information Theory, 2009Co-Authors: Jinhua Jiang, Andrea Goldsmith, Shuguang CuiAbstract:We investigate the Gaussian relay channel where the additive Noises at the relay and destination are Correlated. We obtain achievable rates for the compress-and-forward and decode-and-forward relaying strategies, and compare them to each other and to the capacity upper bound.We show that neither scheme is uniformly best over all channel gains and correlation coefficients. We also derive specific relationships between the channel gains and noise correlations for which one of these schemes is capacity-achieving, thereby increasing the class of relay channels for which capacity is known.
Yuanqing Xia - One of the best experts on this subject based on the ideXlab platform.
-
event triggered state estimation for networked systems with Correlated Noises and packet losses
Isa Transactions, 2020Co-Authors: Cui Zhu, Yuanqing Xia, Juan DaiAbstract:Abstract The event-triggered state estimation for the systems suffering from Correlated Noises and packet losses is considered. A communication mechanism that determines the measurements to be sent or not depending on a specific event-triggered condition is presented to reduce the additional data transmissions. Then a novel event-triggered state estimator related to the trigger threshold and correlation coefficient is proposed. An expected trade-off between the rate of transmission and the estimator performance can be obtained through adjusting the threshold properly, and the influence of noise correlation and packet losses is weakened effectively. The estimator performance is evaluated and certain boundedness conditions for the covariance expectation are obtained. Finally, a target tracking system is supplied to support the relevant results.
-
event triggered sequential fusion estimation with Correlated Noises
Isa Transactions, 2020Co-Authors: Liping Yan, Lu Jiang, Yuanqing XiaAbstract:Abstract This paper focuses on the event-triggered sequential fusion estimation for the multi-sensor systems with Correlated Noises. An event-triggered communication mechanism is introduced to reduce unnecessary energy waste. Considering that measurement noise of different sensors is Correlated with each other and also Correlated with the system noise of the previous step, an event-triggered sequential fusion estimation algorithm is proposed in the sense of linear minimum covariance. The standard values of the correlation parameters are defined to ensure convergence of the designed fusion algorithm and an upper bound of the estimation error covariance is given. A numerical example is used to illustrate the effectiveness of the presented fusion algorithm.
-
multisensor distributed weighted kalman filter fusion with network delays stochastic uncertainties autoCorrelated and cross Correlated Noises
IEEE Transactions on Systems Man and Cybernetics, 2018Co-Authors: Zirui Xing, Liping Yan, Yuanqing Xia, Qinghai GongAbstract:This paper is concerned with the problem of distributed weighted Kalman filter fusion (DWKFF) for a class of multisensor unreliable networked systems (MUNSs) with Correlated Noises. The process noise and the measurement Noises are assumed to be one-step, two-step cross-Correlated, and one-step autoCorrelated, and the measurement Noises of each sensor are one-step cross-Correlated. The stochastic uncertainties in the state and measurements are described by Correlated multiplicative Noises. The MUNSs suffer measurement delay or loss due to their unreliability. Buffers of finite length are proposed to deal with measurement delay or loss, and an optimal local Kalman filter estimator with a buffer of finite length is derived for each subsystem. Based on the new optimal local Kalman filter estimator, the DWKFF algorithm with finite length buffers has been developed which has stronger fault-tolerance ability. Simulation results illustrate the effectiveness of the proposed approaches.
-
multirate multisensor distributed data fusion algorithm for state estimation with cross Correlated Noises
Chinese Control Conference, 2013Co-Authors: Yulei Liu, Liping Yan, Yuanqing Xia, Bo XiaoAbstract:This paper is concerned with the optimal state estimation problem under linear dynamic systems when the sampling rates of different sensors are different. For simplicity, we consider two sensors where one's sampling rate is three times as much as the other's. The Noises of different sensors are cross-Correlated and are also coupled with the system noise of the previous step. By use of the projection theorem and induction hypothesis repeatedly, a distributed fusion estimation algorithm is derived. The algorithm is proven to be distributed optimal in the sense of Linear Minimum Mean Square Error (LMMSE) and can effectively reduces the oscillation existed in the sequential algorithm. Finally, a numerical example is shown to illustrate the effectiveness of the proposed algorithm.
