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第35届中国控制会议

作者： Liping Yan Jun Liu Lu Jiang Yuanqing Xia Bo Xiao Yang Liu Mengyin Fu Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology School of Information and Communication Engineering Beijing University of Posts and Telecommunications

ISBN: (纸本)9781509009107

In the field of target tracking and navigation,multi-sensor data fusion has been widely *** of the data fusion algorithms are built on the premise that the sensor observation information is ***,in practical problems,due to the limitation of communication and sensor fault,etc.,data missing or unreliable measurements will happen *** addition,at present a lot of research is aimed at the situation where measurement noise between various sensors is not relevant,and process noise and measurement noise is *** correlation is more *** this paper,a multi-rate multi-sensor data fusion state estimation algorithm with unreliable observations under correlated noises is presented.A numerical example is given to show the feasibility and effectiveness of the presented algorithm.

关键词： state estimation sequential fusion correlated noise unreliable measurements

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state Estimation and Fault Detection for Nonlinear Dynamic systems

State Estimation and Fault Detection for Nonlinear Dynamic S...

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第37届中国控制会议

作者： Baohua Lu Liping Yan Hongxue Chen Yuanqing Xia Mengyin Fu Bo Xiao Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology School of Information and Communication Engineering Beijing University of Posts and Telecommunications

In practical problems, the dynamic systems are usually nonlinear. In this case, the traditional Kalman filter cannot be used for state estimation or fault detection. The two typical extension based on Kalman filtering framework is the Extended Kalman Filter(EKF) and the Unscented Kalman Filter(UKF). Theoretically speaking, UKF is better than EKF when estimation accuracy is concerned, especially for high degree nonlinear cases. This paper is concerned with the state estimation and fault detection problem for a class of nonlinear dynamic systems. A novel fault detection and analyse method is presented based on the period residual of EKF and UKF. For different kind of faults, mainly, the system parameter error, the sensor/data error, EKF and UKF are used, and the estimation and fault detection effects are compared and analyzed.

关键词： nonlinear system state estimation fault detection parameter error sensor error

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Sequential Fusion and state Estimation for Asynchronous Multirate Multisensor Dynamic systems

Sequential Fusion and State Estimation for Asynchronous Mult...

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第三十三届中国控制会议

作者： Lu Jiang Liping Yan Bo Xiao Yuanqing Xia Mengyin Fu School of Automation Key Laboratory of Intelligent Control and Decision of Complex SystemsBeijing Institute of Technology School of Information and Communication Engineering Beijing University of Posts and Telecommunications

A sequential fusion and state estimation algorithm for an asynchronous multirate multisensor dynamic system is presented in this *** dynamic system at the finest scale is *** are multiple sensors observing a single target independently with different sampling rates,and the observations are obtained *** present algorithm is shown to be more effective and efficient than the existed *** on a radar tracking system with three sensors are done and show the effectiveness of the present algorithm.

关键词： state estimation,sequential fusion,asynchronous,multirate multisensor,Kalman filter

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The Fusion of Multisensor Images with Multi-resolutions

The Fusion of Multisensor Images with Multi-resolutions

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第三十三届中国控制会议

作者： Liping Yan Bo Xiao Yuanqing Xia Meiling Wang Mengyin Fu School of Automation Key Laboratory of Intelligent Control and Decision of Complex SystemsBeijing Institute of Technology School of Information and Communication Engineering Beijing University of Posts and Telecommunications

ISBN: (纸本)9781479947249

Images taken by different sensors at different time instant with different resolutions are formulated by state space models, and are fused by use of Multiscale Kalman Filter(MKF). The effectiveness of the presented algorithm is shown by comparing it with the wavelet based method through experiments, where four performance measures are used. The performance evaluation indices are the root mean square errors(RMSE), the information entropy(Entropy), the space frequency(SF) and the space visibility(SV). Theretical analysis and experimental results show the effectiveness of the presented algorithm.

关键词： Image fusion,multiresolution,multiscale Kalman filter,state space model

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Graph Optimization Based Long-distance GPS/IMU Integrated Navigation

Graph Optimization Based Long-distance GPS/IMU Integrated Na...

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第三十八届中国控制会议

作者： Xiaojie Gu Fengdi Zhang Jian Xu Qing Yuan Hongwei Ma Xiangdong Liu Zhen Li School of Automation and Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing Aerospace Automatic Control Institute National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing

The accurate position estimation plays an critical role in the autonomous navigation for Micro Aerial Vehicles(MAV).Global positioning system(GPS) and inertial measurement unit(IMU) are two common sensors for navigation widely used on MAVs in the urban environment. Both of them have its distinct disadvantages that the GPS is susceptible to environmental interference and the IMU has accumulative errors. To overcome these problems, a GPS/IMU integrated system based on the factor graph optimization is developed in this paper. Unlike the conventional extended Kalman filter(EKF)-based method, the graph optimization method takes the whole trajectory into consideration so that it can achieve enough accuracy even after a long distance. Furthermore, the IMU preintegration method is used to avoid the repeated computation of high-rate IMU *** with the EKF method, the experimental results on the Zurich urban micro aerial vehicle dataset show the superior accuracy of the proposed factor graph optimization algorithm.

关键词： Global positioning system(GPS) inertial measurement unit(IMU) IMU preintegration graph optimization integrated navigation

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Multirate Multisensor Distributed Data Fusion Algorithm for state Estimation with Cross-Correlated Noises

Multirate Multisensor Distributed Data Fusion Algorithm for ...

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第三十二届中国控制会议

作者： Yulei Liu Liping Yan Yuanqing Xia Mengyin Fu Bo Xiao School of Automation Key Laboratory of Intelligent Control and Decision of Complex SystemsBeijing Institute of Technology School of Information and Communication Engineering Beijing University of Posts and Telecommunications

ISBN: (纸本)9781479900305

This paper is concerned with the optimal state estimation problem under linear dynamic systems when the sampling rates of different sensors are *** simplicity,we consider two sensors where one's sampling rate is three times as much as the other'*** 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 *** 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 ***,a numerical example is shown to illustrate the effectiveness of the proposed algorithm.

关键词： state estimation Distributed data fusion Cross-correlated noises Multirate

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Low frequency positive real control for delta operator systems

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Automatica 2012年第8期48卷 1791-1795页

作者： Hongjiu Yang Yuanqing Xia School of Automation Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing 100081 China Institute of Electrical Engineering Yanshan University Qinhuangdao 066004 China

A strictly positive real control problem for delta operator systems in a low frequency range is presented by using the generalized Kalman–Yakubovic˘–Popov lemma. The objective of the strictly positive real control problem is to design a controller such that the transfer function is strictly positive real and the resulting closed-loop system is stable. Sufficient conditions for the low frequency strictly positive real controller of the closed-loop delta operator systems are presented in terms of solutions to a set of linear matrix inequalities. A numerical example is given to illustrate the effectiveness and potential for the developed techniques.

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Neural network structure design based on N-gauss activation function1

arXiv

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arXiv 2021年

作者： Lu, Xiangri Ma, Hongbin Zhang, Jingcheng State Key Laboratory of Intelligent Decision and Control for Complex Systems Beijing Institute ofTechnology Beijing China Institute of Pattern Recognition and Intelligent Systems School of Automation Beijing Institute ofTechnology China Institute of Navigation Guidance and Control School of Automation Beijing Institute of

Recent work has shown that the activation function of the convolutional neural network can meet the Lipschitz condition, then the corresponding convolutional neural network structure can be constructed according to the scale of the data set, and the data set can be trained more deeply, more accurately and more effectively. In this article, we have accepted the experimental results and introduced the core block N − Gauss, N − Gauss, and Swish (Conv1, Conv2, FC1) neural network structure design to train MNIST, CIFAR10, and CIFAR100 respectively. Experiments show that N − Gauss gives full play to the main role of nonlinear modeling of activation functions, so that deep convolutional neural networks have hierarchical nonlinear mapping learning capabilities. At the same time, the training ability of N − Gauss on simple one-dimensional channel small data sets is equivalent to the performance of ReLU and Swish. Copyright © 2021, The Authors. All rights reserved.

关键词： Gaussian distribution

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control of Spatially Interconnected systems with Random Communication Losses

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Acta Automatica Sinica 2010年第2期36卷 258-266页

作者： Hui LI Qing-He WU Huang HUANG School of Automation Beijing Institute of Technology Beijing 100081 P. R. China Key Laboratory of Complex System Intelligent Control and Decision (Beijing Institute of Technology) Ministry of Education Beijing 100081 P. R. China

This paper deals with analysis and synthesis problems of spatially interconnected systems where communicated information may get lost between subsystems. Spatial shift operator and temporal forward shift operator are introduced to model the interconnected systems as discrete time-space multidimensional linear systems with Markovian jumping parameters which reflect the state of communication channels. To ensure the whole system s well-posedness and mean square stability for a given packet loss rate, a condition is derived through analysis. Then a procedure of designing distributed dynamic output feedback controllers is proposed. The controllers have the same structure as the plants and are solved within the linear matrix inequality (LMI) framework. Finally, we apply these results to study the effect of communication losses on the multiple vehicle platoon control system, which further illustrates the effectiveness of the proposed model and method.

关键词： Spatially interconnected system communication loss Markovian jump linear system linear matrix inequality (LMI)

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A Two-stage ARMAX Identification Approach Based on Bias-eliminated Least Squares and Parameter Relationship between MA Process and Its Inverse

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Acta Automatica Sinica 2012年第3期38卷 491-496页

作者： Bin XIN Jie CHEN School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing 100081 China Decision and Cognitive Sciences Research Centre Manchester Business School University of Manchester M15 6PB Manchester UK

In this paper, a two-stage approach is proposed for the parameter identification of autoregressive moving average with exogenous (ARMAX) variable model. The proposed approach identifies the autoregressive part with exogenous variable (ARX) by a bias-eliminated least squares method, and the moving average (MA) part by utilizing the parameter relationship between MA process and its inverse. Finally, the noise variance can be computed by using the identified MA parameters. Numerical simulations validate the effectiveness of the proposed approach.

关键词： Autoregressive moving average with exogenous (ARMAX) model parameter identification inverse model least-squares method bias compensation

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