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2009中国控制与决策会议

作者： jian xu,jianxun li department of automation,shanghai jiao tong university,shanghai,china 200240

＜正＞Sparse Wiener Chaos approximations of Zakai equation is *** objective is to optimize an approach to nonlinear filtering based on the Cameron-Martin version of Wiener Chaos Expansion（WCE）.The error of the approximation is *** main feature of Wiener chaos expansion is that it allows one to separate the computations involving the observations from those dealing only with the system parameters and to shift the latter *** sparse truncation can reduce the number of the WCE coefficients dramatically while keeping the same asymptotic convergence rate as the simple truncation.

关键词： Nonlinear Filtering Zakai Equation Wiener Chaos Simple Truncation Sparse Truncation

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Data fusion for target tracking in wireless sensor networks using quantized innovations and Kalman filtering

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Science china(Information Sciences) 2012年第3期55卷 530-544页

作者： xu jian1,2 , li jianxun1,2 & xu Sheng3 1department of automation, Science and Technology on Avionics Integration Laboratory, shanghai jiao tong university, shanghai 200240, china 2department of automation, System Control and Information Processing, Ministry of Education of china, shanghai jiao tong university, shanghai 200240, china 3School of Mechanical Engineering, shanghai jiao tong university, shanghai 200240, china department of automation Science and Technology on Avionics Integration Laboratory Shanghai Jiao Tong University Shanghai China department of automation System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China

A novel networked data-fusion method is developed for the target tracking in wireless sensor networks (WSNs). Specifically, this paper investigates data fusion scheme under the communication constraint between the fusion center and each sensor. Such a message constraint is motivated by the bandwidth limitation of the communication links, fusion center, and by the limited power budget of local sensors. In the proposed scheme, each sensor collects one noise-corrupted sample, performs a quantizing operation, and transmits quantized message to the fusion center. Then the fusion center combines the received quantized messages to produce a final estimate. The novel data-fusion method is based on the quantized measurement innovations and decentralized Kalman filtering (DKF) with feedback. For the proposed algorithm, the performance analysis of the estimation precision is provided. Finally, Monte Carlo simulations show the effectiveness of the proposed scheme.

关键词： data fusion target tracking limited bandwidth quantized innovation Kalman filtering

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Target tracking in wireless sensor networks using adaptive measurement quantization

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Science china(Information Sciences) 2012年第4期55卷 827-838页

作者： ZHOU Yan 1,2,li jianxun 2 & WANG Dongli 1 1 Colleage of Information Engineering,Xiangtan university,Xiangtan 411105,china 2 department of automation,shanghai jiao tong university,shanghai 200240,china Colleage of Information Engineering Xiangtan University Xiangtan China department of automation Shanghai Jiao Tong University Shanghai China

Quantization/compression is usually adopted in wireless sensor networks (WSNs) since each sensor node typically has very limited power supply and communication *** consider the problem of target tracking in a WSN with quantized measurements in this *** is focused on the design of measurement quantizer with adaptive *** on the probability density function (PDF) of the signal amplitude measured at a random location and by maximizing the entropy,an adaptive design method for quantization thresholds is *** to the nonlinear measuring and quantization models,particle filtering (PF) is adopted in the fusion center (FC) to estimate the target *** Cram'er-Rao lower bounds (CRLBs) for tracking accuracy using quantized measurements are also ***,a simulation example on tracking single target with noisy circular trajectories is provided to illustrate the effectiveness of the proposed approach.

关键词： target tracking wireless sensor network adaptive quantization posterior Cram’er-Rao lower bound

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State Estimation with Quantized Measurements in Wireless Sensor Networks

State Estimation with Quantized Measurements in Wireless Sen...

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第二十九届中国控制会议

作者： xu jian,li jianxun department of automation,shanghai jiao tong university,shanghai,200240,P.R.china

The problem of state estimation with quantized measurements is *** to the nonlinearity of the quantizer,estimating the system state is a nonlinear and non-Gaussian estimation problem even if the system is linear and Gaussian.A novel algorithm for approximate minimum mean square error（MMSE）state estimation with quantized measurement is *** algorithm is based on careful analysis for the quantized *** effective information extraction from the quanti-tative measurement,the true measurement value is reestablished *** evaluation and comparison of the proposed algorithm with the existing methods by simulation of a typical tracking scenario in Wireless Sensor Networks（WSN） systems are *** numerical results show that the tracking algorithm is effective.

关键词： Wireless Sensor Networks Quantized Observations Minimum Mean Squared Error(MMSE) Unscented Trans-form Decentralized State Estimation

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Implementation of Dynamic Matrix Control on FPGA

Implementation of Dynamic Matrix Control on FPGA

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第二十九届中国控制会议

作者： LAN jian,li Dewei,XI Yugeng shanghai jiao tong university department of automation,shanghai 200240,P.R.china

To extend the application of model predictive control（MPC）to low level control facilities and cheap computation devices,this paper studies the implementation of MPC Controller on *** the Dynamic Matrix Control（DMC）algorithm,the main design idea and the implemental strategy of the predictive controller are introduced based on a FPGA’s embedded *** performance tests show that both the computation efficiency and the accuracy of the proposed controller can be satisfied,due to the parallel computing capability of FPGA.

关键词： Model Predictive Control Dynamic Matrix Control Quadratic Programming Active Set Programmable Logic Device FPGA

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Robust Adaptive Kalman Filtering For Target Tracking With Unknown Observation Noise

Robust Adaptive Kalman Filtering For Target Tracking With Un...

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第24届中国控制与决策会议

作者： Yongchen li, jianxun li department of automation, shanghai jiao tong university, and Key Laboratory of System Control and Information Processing, Ministry of Education of china, shanghai 200240

The Kalman filter （KF） is widely used in the field of target tracking. In practical target tracking systems through, the observation noise is often unknown and characterized by heavier tails named outliers. That will affect the performance of target tracking seriously and even lead to filtering divergence. To overcome this problem, a novel robust Kalman filter （RKF） is proposed based on the maximum a posteriori （MAP） estimation to observation outliers. In addition, the adaptive estimate of observation noise variance is also given based on the weighted correlation innovation （WCI） sequences of output of a steady state Kalman filter （SSKF）. Finally, a robust adaptive Kalman filter （RAKF） algorithm is raised by implementing RKF and adaptive estimate of simultaneously. The feasibility of the algorithm is demonstrated by an example of target tracking with simulation.

关键词： Kalman filter outlier robustness variance estimation adaptability target tracking

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Interactive Multiple Kalman Filters based 3-D Target Tracking for Two Airborne Platforms with Bearings-Only Measurements

Interactive Multiple Kalman Filters based 3-D Target Trackin...

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第24届中国控制与决策会议

作者： ZHOU Yan 1 , li jianxun 2 1. College of Information Engineering, Xiangtan university, Xiangtan 411105, china2. department of automation, shanghai jiao tong university, shanghai 200240, china

Target tracking by two airborne platforms with bearings-only measurements has obtained distinct interest recently. It is a nonlinear problem that Kalman filter (KF) can not be applied directly. Using extended Kalman filter (EKF) to solve this problem will get high computational burden involved because the Jacoby and Hessian matrices for the measuring equation should be calculated online. In this paper, the triangular ranging formula is derived for two bearings-only platforms first. Then the location error is proved to be zero mean Gaussian noises. This enables the traditional Kalman filter applicable. To further improve the tracking accuracy, interactive multiple model (IMM) is adopted to estimate the target state from the converted measurements. Simulation for both non-maneuvering and maneuvering target is also included to illustrate the proposed approach has high accuracy with much lower computational burden.

关键词： Bearings-only Target tracking Interactive multiple model Kalman filter.

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Low Cycle Fatigue life prediction of a 300MW Steam Turbine Rotor using a New nonlinear Accumulation Approach

Low Cycle Fatigue Life prediction of a 300MW Steam Turbine R...

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第24届中国控制与决策会议

作者： Yong-jian Sun li-sheng Hu * department of automation, shanghai jiao tong university, and Key Laboratory of System Control And Information Processing, Ministry of Education of china, shanghai 200240, PR china

A new nonlinear model is proposed to assess the low cycle fatigue life of a steam turbine rotor . Temperature field and stress field of a 300MW steam turbine under normal sliding parameters stop is computed using Finite element analysis. For the reason considering the cyclic stress-strain relation , the new model shows a conservative character . The result is compared with that from the linear accumulation theory . The comparison shows that this new nonlinear model describes the damage accumulation more precisely and reasonably in engineering .

关键词： low cycle fatigue continuum damage mechanics steam turbine rotor finite element analysis

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An Analysis Method of Model Reduction Error Based on H∞ Norm

An Analysis Method of Model Reduction Error Based on H∞ Nor...

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第24届中国控制与决策会议

作者： Shan Han 1 , jianxun li 2 ,Zhi Zhang 3 department of automation, shanghai jiao tong university, and Key Laboratory of System Control and Information Processing, Ministry of Education of china, shanghai 200240

Because of the limitation of linear controllers at the present stage, model reduction of complicated system still plays an important role in engineering application. During the research of electric load simulator with a high-order nonlinear model, no effective method is presented to evaluate the effect of different reduction ways. To solve this problem, an analysis method of model reduction error based on H∞ norm is proposed and applied in this paper. A synthesis error can be obtained by this method including linearization error and order reduction error. Compared with the figures in common use, the quantized result providing the basis for performance evaluation of model reduction is more intuitive and reliable. As the experimental verification, the superiority of Balanced Truncation used in the model reduction of electric load simulator is testified by this proposed method.

关键词： High-order Nonlinear System Model Reduction Error Analysis H∞ Norm

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Analysis of quantization noise and state estimation with quantized measurements

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控制理论与应用（英文版） 2011年第1期9卷 66-75页

作者： xu, jian li, jianxun xu, Sheng department of automation Shanghai Jiao Tong University Shanghai 200240 China College of Mechanical Engineering Chongqing University Chongqing 400030 China

The approximate correction of the additive white noise model in quantized Kalman filter is investigated under certain conditions. The probability density function of the error of quantized measurements is analyzed theoretically and experimentally. The analysis is based on the probability theory and nonparametric density estimation technique, respectively. The approximator of probability density function of quantized measurement noise is given. The numerical results of nonparametric density estimation algorithm demonstrate that the theoretical conclusion is reasonable. Based on the analysis of quantization noise, a novel algorithm for state estimation with quantized measurements also is proposed. The algorithm is based on the least-squares estimator and unscented transform. By least-squares estimator, the effective information is extracted from the quantized measurements. Also, using the information to update the estimated state can give a better estimation under the influence of quantization. The root mean square error (RMSE) of the proposed algorithm is compared with the RMSE of the existing methods for a typical tracking scenario in wireless sensor networks systems. Simulations provide a strong evidence that this tracking algorithm could indeed give us a more precise estimated result.

关键词： Wireless sensor networks Quantized observations Nonparametric density estimation Least-squares method Unscented transform

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