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Stochastic stability of the derivative unscented kalman filter

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Chinese Physics B 2015年第7期24卷 64-73页

作者：胡高歌高社生种永民高兵兵 School of Automatics Northwestern Polytechnical University School of Aerospace Mechanical and Manufacturing EngineeringRMIT University

This is the second of two consecutive papers focusing on the filtering algorithm for a nonlinear stochastic discretetime system with linear system state equation. The first paper established a derivative unscented kalman filter（DUKF） to eliminate the redundant computational load of the unscented kalman filter（UKF） due to the use of unscented transformation（UT） in the prediction process. The present paper studies the error behavior of the DUKF using the boundedness property of stochastic processes. It is proved that the estimation error of the DUKF remains bounded if the system satisfies certain conditions. Furthermore, it is shown that the design of the measurement noise covariance matrix plays an important role in improvement of the algorithm stability. The DUKF can be significantly stabilized by adding small quantities to the measurement noise covariance matrix in the presence of large initial error. Simulation results demonstrate the effectiveness of the proposed technique.

关键词： nonlinear stochastic system stochastic process unscented kalman filter stochastic stability

Combining unscented kalman filter and wavelet neural network for anti-slug

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Petroleum Science 2023年第6期20卷 3752-3765页

作者： Chuan Wang Long Chen Lei Li Yong-Hong Yan Juan Sun Chao Yu Xin Deng Chun-Ping Liang Xue-Liang Zhang Wei-Ming Peng School of Mechanical Engineering Southwest Petroleum UniversityChengdu610500SichuanChina Energy Equipment Institute Southwest Petroleum UniversityChengdu610500SichuanChina CNPC National Engineering Research Center for Oil and Gas Drilling Equipment Co.LTD Baoji721000ShaanxiChina Oil Production Service Co. CETS LTD.Tianjin300000China

The stability of the subsea oil and gas production system is heavily influenced by slug flow. One successful method of managing slug flow is to use top valve control based on subsea pipeline pressure. However, the complexity of production makes it difficult to measure the pressure of subsea pipelines, and measured values are not always accessible in real-time. The research introduces a technique for integrating unscented kalman filter (UKF) and Wavelet Neural Network (WNN) to estimate the state of subsea pipeline pressure using historical data and a state model. The proposed method treats multiphase flow transport as a nonlinear model, with a dynamic WNN serving as the state observer. To achieve real-time state estimation, the WNN is included into the UKF algorithm to create a WNN-based UKF state equation. Integrate WNN and UKF in a novel way to predict system state accurately. The simulated results show that the approach can efficiently predict the inlet pressure and manage the slug flow in real-time using the riser's top pressure, outlet flow and valve opening. This method of estimate can significantly increase the control effect.

关键词： State estimation Stable control Method fusion Wavelet neural network unscented kalman filter

Inferring nonlinear lateral flow immunoassay state-space models via an unscented kalman filter

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Science China(Information Sciences) 2016年第11期59卷 73-82页

作者： Nianyin ZENG Zidong WANG Hong ZHANG Department of Instrumental and Electrical Engineering Xiamen University Faculty of Engineering King Abdulaziz University Department of Computer Science Brunel University London

This paper is concerned with the problem of learning structure of the lateral flow immunoassay(LFIA) devices via short but available time series of the experiment measurement. The model for the LFIA is considered as a nonlinear state-space model that includes equations describing both the biochemical reaction process of LFIA system and the observation output. Especially, the time-delays occurring among the biochemical reactions are considered in the established model. Furthermore, we utilize the unscented kalman filter(UKF)algorithm to simultaneously identify not only the states but also the parameters of the improved state-space model by using short but high-dimensional experiment data in terms of images. It is shown via experiment results that the UKF approach is particularly suitable for modelling the LFIA devices. The identified model with time-delay is of great significance for the quantitative analysis of LFIA in both the accurate prediction of the dynamic process of the concentration distribution of the antigens/antibodies and the performance optimization of the LFIA devices.

关键词： lateral flow immunoassay biochemical reaction networks modelling unscented kalman filter

Sampling strong tracking nonlinear unscented kalman filter and its application in eye tracking

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Chinese Physics B 2010年第10期19卷 324-332页

作者：张祖涛张家树 School of Mechanical Engineering Southwest Jiaotong University Sichuan Provincial Key Laboratory of Signal & Information Processing Southwest Jiaotong University

The unscented kalman filter is a developed well-known method for nonlinear motion estimation and tracking. However, the standard unscented kalman filter has the inherent drawbacks, such as numerical instability and much more time spent on calculation in practical applications. In this paper, we present a novel sampling strong tracking nonlinear unscented kalman filter, aiming to overcome the difficulty in nonlinear eye tracking. In the above proposed filter, the simplified unscented transform sampling strategy with n＋ 2 sigma points leads to the computational efficiency, and suboptimal fading factor of strong tracking filtering is introduced to improve robustness and accuracy of eye tracking. Compared with the related unscented kalman filter for eye tracking, the proposed filter has potential advantages in robustness, convergence speed, and tracking accuracy. The final experimental results show the validity of our method for eye tracking under realistic conditions.

关键词： unscented kalman filter strong tracking filtering sampling strong tracking nonlinearunscented kalman filter eye tracking

Prediction of the coupled heat radiation and conduction parameters and boundary condition using the unscented kalman filter

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Science China(Technological Sciences) 2020年第3期63卷 422-433页

作者： WEN Shuang QI Hong WANG YiFei REN YaTao WEI LinYang RUAN LiMing School of Energy Science and Engineering Harbin Institute of TechnologyHarbin 150001China Key Laboratory of Aerospace Thermophysics Ministry of Industry and Information TechnologyHarbin 150001China

The boundary emissivity, thermal conductivity, and boundary time-varying heat flux in the participating media are retrieved in this work. In the forward model, the coupled radiation-conduction heat transfer in the participating medium is resolved by the finite volume method combined with discrete ordinates method. The inverse model utilizes the temperature signals at the appropriate position of the medium as the known information and uses the unscented kalman filter(UKF) as an optimization tool to reconstruct the boundary emissivity, thermal conductivity, and boundary time-varying heat flux. It is found that when the emissivity, thermal conductivity, and boundary time-varying heat flux are reconstructed simultaneously, only the temperature information of two locations is required. The influence of the measurement noise, sampling interval, absorption coefficient,process noise covariance, measurement noise covariance on the accuracy and stability of the retrieval results is investigated in detail. All the reconstruction results indicate that the UKF technique is effective and robust for estimating the photothermal parameters and boundary condition of the radiation-conduction heat transfer problems.

关键词： inverse heat transfer thermal conductivity boundary emissivity time-varying heat flux unscented kalman filter participating media

Instantaneous spectrum estimation of earthquake ground motions based on unscented kalman filter method

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Applied Mathematics and Mechanics(English Edition) 2007年第11期28卷 1535-1543页

作者：李英民董银峰赖明 College of Civil Engineering Chongqing University Key Laboratory of New Technique for Construction of Cities in Mountain Area Chongqing UniversityChongqing 400045P.R.China Department of Science and Technology Ministry of Construction

Representing earthquake ground： motion as time varying ARMA model, the instantaneous spectrum can only be determined by the time varying coefficients of the corresponding ARMA model. In this paper, unscented kalman filter is applied to estimate the time varying coefficients. The comparison between the estimation results of unscented kalman filter and kalman filter methods shows that unscented kalman filter can more precisely represent the distribution of the spectral peaks in time-frequency plane than kalman filter, and its time and frequency resolution is finer which ensures its better ability to track the local properties of earthquake ground motions and to identify the systems with nonlinearity or abruptness. Moreover, the estimation results of ARMA models with different orders indicate that the theoretical frequency resolving power of ARMA model which was usually ignored in former studies has great effect on the estimation precision of instantaneous spectrum and it should be taken as one of the key factors in order selection of ARMA model.

关键词： unscented kalman filter earthquake ground motion instantaneous spectrum time varying ARMA model

An aperiodic phenomenon of the unscented kalman filter in filtering noisy chaotic signals

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Progress in Natural Science:Materials International 2007年第10期17卷 1235-1240页

作者： Fan Hongjuan1, Feng Jiuchao1,2, Xie Shengli2 and Wang Shiyuan1(1. Faculty of Electronic and Information Engineering ,Southwest University, Chongqing 400715, China 2. School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China) Faculty of Electronic and Information Engineering Southwest University Chongqing 400715 China School of Electronic and Information Engineering South China University of Technology Guangzhou 510641 China

A non-periodic oscillatory behavior of the unscented kalman filter (UKF) when used to filter noisy contaminated chaotic signals is reported. We show both theoretically and experimentally that the gain of the UKF may not converge or diverge but oscillate aperiodically. More precisely, when a nonlinear system is periodic, the kalman gain and error covariance of the UKF converge to zero. However, when the system being considered is chaotic, the kalman gain either converges to a fixed point with a magnitude larger than zero or oscillates aperiodically.

关键词： chaos unscented kalman filter Lyapunov exponents aperiodicity.

Vehicle State and Bias Estimation Based on unscented kalman filter with Vehicle Hybrid Kinematics and Dynamics Models

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Automotive Innovation 2023年第4期6卷 571-585页

作者： Shouren Zhong Yang Zhao Linhe Ge Zitong Shan Fangwu Ma State Key Laboratory of Automotive Simulation and Control Jilin UniversityChangchun 130022China

In recent years,vehicle state estimation methods incorporating different vehicle models have received extensive *** the vehicle is disturbed by external forces not considered in traditional vehicle models(for example,a certain slope,or wind resistance different from theoretical calculation),the problem of model mismatch will occur,which leads to the inaccurate estimation of the vehicle *** solve this problem,an unscented kalman filter(UKF)algorithm is used to fuse inertial navigation data with the vehicle hybrid model in this *** hybrid model introduces a switching strategy that fuses the vehicle kinematics and the dynamics models while augmenting biases that need to be estimated in the vehicle *** switching strategy resolves the integration divergence problem of vehicle dynamics models at low speeds and the inaccurate estimation of vehicle kinematics models at high *** experiments demonstrate that the proposed method can accurately estimate biases induced by external forces,enhancing the accuracy and confidence of states by eliminating errors caused by these *** robustness of the method is validated in vehicle verification platform experiments,where errors in vehicle lateral speed and yaw rate are reduced by 9.7 cm/s and 0.012°/s,respectively,under large curvature maneuvers,and 9.6 cm/s and 0.004°/s under quarter-turn *** proposed method significantly improves lateral speed and vehicle position accuracies.

关键词： Vehicle state estimation Vehicle hybrid models unscented kalman filter Integration stability

Blind Channel Equalization Algorithm Based on Dual unscented kalman filter for Chaotic Multi-Input Multi-Output Communication Systems

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Transactions of Tianjin University 2012年第1期18卷 33-37页

作者： Hu Zhihui FENG Jiuchao School of Electronic and Information Engineering South China University of TechnologyGuangzhou 510641China

To reduce channel noise,fading,and inter-user interference effectively in the chaotic communication systems with multi-user,a blind channel equalization algorithm based on dual unscented kalman filter algorithm is *** that the coefficients of a multi-input multi-output (MIMO) channel can be described by an autoregressive model,two separate state-space representations are used for the signals and *** two unscented kalman filters are used to estimate chaotic signals and channel coefficients *** simulation results indicate that the algorithm can effectively track the coefficients of the multi-path fading channel in chaotic MIMO communication systems at a fast convergence speed.

关键词： chaotic MIMO system blind channel equalization unscented kalman filter