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检索条件"主题词=Particle filtering algorithm"

共 61 条记录，以下是21-30 订阅

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Continuous strain prediction for fatigue assessment of an offshore wind turbine using Kalman filtering techniques 7

Continuous strain prediction for fatigue assessment of an of...

引用

IEEE Workshop on Environment Energy Structural Monitoring Systems

作者： Maes, K. De Roeck, G. Lombaert, G. Iliopoulos, A. Van Hemelrijck, D. Devriendt, C. Guillaume, P. Katholieke Univ Leuven Dept Civil Engn B-3001 Leuven Belgium VUB Dept Mech Mat & Construct B-1050 Brussels Belgium VUB Acoust & Vibrat Res Grp B-1050 Brussels Belgium

ISBN: (纸本)9781479982158

Offshore wind turbines are exposed to continuous wind and wave excitation. The continuous monitoring of high periodic strains at critical locations is important to assess the remaining lifetime of the structure. Some of the critical locations are not accessible for direct strain measurements, e.g. at the mud-line, 30 meter below the water level. Response estimation techniques can then be used to estimate the response at unmeasured locations from a limited set of response measurements and a system model. This paper shows the application of a Kalman filtering algorithm for the estimation of strains in the tower of an offshore monopile wind turbine in the Belgian North Sea. The algorithm makes use of a model of the structure and a limited number of response measurements for the prediction of the strain responses. It is shown that the Kalman filter algorithm is able to account for the different types of excitation acting on the structure in operational conditions, in this way yielding accurate strain estimates that can be used for continuous fatigue assessment of the wind turbine.

关键词： Fatigue assessment offshore Wind Energy Conversion Systems Kalman filtering turbines WAVE INTERFERENCE filtering algorithms image filtering algorithm particle filtering algorithm Operating conditions

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State estimation and optimization of large-scale dynamic systems with improved particle filters

State estimation and optimization of large-scale dynamic sys...

引用

作者： Shi, Xiaoran University of Miami

学位级别：Ph.D.

This thesis explores novel methodologies for improving the particle filtering algorithm, and tackles state estimation and optimization problems of large-scale dynamic systems through the use of the improved particle filters. First of all, an importance density selection scheme for the particle filtering algorithm is first proposed based on the minimum relative entropy and the theorem of Taylor series expansion. By considering both the transition prior (previous states) and the likelihood (measurements), the proposed density selection scheme improves the performance of the particle filters especially when the measurements appear in the tail of the prior or the prior differs significantly from the posterior. Secondly, a particle filtering-based optimization algorithm for the multi-objective optimization problem is developed to establish a connection between the population-based optimization methods and the particle filtering algorithm. Here, the deterministic multi-objective optimization problem is represented using a state-space model. Then, samples (i.e., candidate solutions) are drawn from a distribution function, which can be computed recursively based on the performance of the prior particle set and the newly arrived observations. As the iteration progresses, the distribution function becomes more and more concentrated on the promising region of the solution space, indicating the convergence capability of the proposed algorithm. When it comes to the practical contribution, two popular state estimation problems are studied. Specifically, a daily electricity demand forecasting problem is addressed by the way of incorporating the particle filters that embed the proposed density selection scheme into a developed state-space model. In a similar vein, a problem of low elevation target tracking over the sea surface in the presence of multipath effects is considered, and a corresponding tracking mechanism is proposed based on the state-space modeling methodologies and th

关键词： state estimation multi-objective optimization particle filtering algorithm

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A particle Filter Tracking algorithm of Multi-features Fusion Based on Energy Cumulant

A Particle Filter Tracking Algorithm of Multi-features Fusio...

引用

IEEE International Conference on Signal Processing, Communications and Computing

作者： Liangkai Shao Huanxin Zou Lin Lei Coll. of Electron. Sci. & Eng. Nat. Univ. of Defense Technol. Changsha China

ISBN: (纸本)9781479989218

A particle filter tracking algorithm of multi-features fusion based on energy cumulant is proposed in this paper. This algorithm mainly focuses on the dim target tracking problem under complex background of infrared image sequence, and analyzes the different features of infrared dim targets. Since the particle filtering algorithm gives the advantage of multi-features fusion, this paper combines the four features, such as gray scale value, local entropy feature, local energy feature and high-frequency histogram feature, to calculate the particle weights which greatly improves the tracking accuracy, and uses energy cumulant algorithm to suppress the background and improve the signal to clutter ratio (SCR). The experimental results on both synthetic and real-world data demonstrate that, the proposed algorithm has substantial improvements in terms of tracking accuracy and robustness over the traditional particle filtering algorithms.

关键词： feature extraction image sequences infrared imaging particle filtering (numerical methods) target tracking SCR complex background energy cumulant algorithm infrared image sequence multifeatures fusion particle filter tracking algorithm particle filtering algorithm signal to clutter ratio target tracking problem Entropy filtering Histograms Mathematical model Robustness Target tracking Thyristors energy cumulant high-frequency histogram multi-features fusion particle filtering particle filtering algorithm particle filters Thyristors Target tracking image sequences Infrared imaging histograms entropy Filtration Feature extraction tracking accuracy Mathematical Model Fusion reactions FUSION fusion (melting)

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Parallel Recursive Estimation using Monte Carlo and Orthogonal Series Expansions

Parallel Recursive Estimation using Monte Carlo and Orthogon...

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American Control Conference

作者： Olov Rosen Alexander Medvedev Department of Information Technology Uppsala University Uppsala SE-751 05 Sweden

ISBN: (纸本)9781479917730

Parallelizability of an algorithm is nowadays a highly desirable property as computer hardware is becoming increasingly parallel. In this paper, a formulation of the particle filtering algorithm, suitable for parallel or distributed computing, is proposed. From the particle set, a series expansion is fitted to the posterior probability density function. The global information provided by the particles can in this way be expressed by a few informative coefficients that can be efficiently communicated between the local processing units. Experiments on a shared-memory multicore processor using up to eight cores show that a linear speedup in the number of used cores is achieved.

关键词： recursive estimation Series expansions orthogonal series Parallel Lines Computer hardware Distributed computing a posteriori probability particle filtering algorithm Multi-core processors Hypoderma

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Research of an improved particle filter algorithm

Research of an improved particle filter algorithm

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Yong-Wei Li Ming-Xing Chen College of electrical engineering Hebei University of Science and Technology Shijiazhuang China

ISBN: (纸本)9781467372220

Aiming at the problem of the low accuracy, which is caused by the scarcity of particles and the local optimization in particle filter, this paper puts forward an improved particle filter based on particle swarm optimization to overcome the shortcomings of the scarcity of particles and the local optimization in particle filter. At last, the improved particle filter algorithm is applied to the process of the synthetic ammonia, aiming at the optimization of the complex industrial process which contains the characteristics of nonlinearity, non-Gaussian, large delay, strong coupling and it is difficult to establish a control model online, thus it can make that the complex industrial process reflects the true state more accurately. The simulation results show that it improves the precision of the algorithm when compared with the simple particle filter algorithm, and in the complex industrial process of synthetic ammonia the algorithm has the practical application value and also provides an effective way for the optimization control of a class of complex industrial process.

关键词： particle filter particle swarm optimization Synthetic ammonia particle filters particle filtering algorithm Industrial processes particle swarm optimization local optimization SCARCITY

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Extension particle filtering algorithm for State and Parameter Estimation in Dynamic Control Process

Extension Particle Filtering Algorithm for State and Paramet...

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21st Chinese Control and Decision Conference

作者： Gao Xian-zhong Hou Zhong-xi Ren Bo-tao Natl Univ Def Technol Coll Aerosp & Mat Engn Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781424427239

Aiming to solve the problem of unknown parameters estimation in nonlinear and/or non-Gaussian dynamic system, Extension particle filtering(EPF) algorithm was proposed. EPF algorithm modeled unknown parameters by Gaussian random walk process, regarded unknown parameters in dynamic system as a part of state variations, and then estimated the state variations in extension nonlinear dynamic system by particle filtering algorithm. In order to improve the estimate precision of unknown parameters by utilizing observable information effectively, a new important density was purposed to instead of Bootstrap filter, further more, it avoided transformation about covariance. In order to solve the problem that covariance augmented infinitely with time in Gaussian random walk model, and Kernel smooth factor restrained covariance excessively so as to the values of estimate parameters could not access the values of true parameters sufficiently, the Gradually Reduce(GR) factor was purposed to instead of Kernel factor. At the end of this paper, the effectiveness and availability of purposed algorithm was validated by an exemplum.

关键词： particle filtering algorithm Parameters estimation Important density Smooth factor

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Truncated unscented particle filter for dealing with non-linear inequality constraints 4

Truncated unscented particle filter for dealing with non-lin...

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4th Conference of the Sensor Signal Processing for Defence (SSPD)

作者： Yu, Miao Chen, Wen-Hua Chambers, Jonathon Univ Loughborough Aeronaut & Automot Engn Dept Loughborough LE11 3TU Leics England Loughborough Univ Sch Elect Elect & Syst Engn Loughborough LE11 3TU Leics England

ISBN: (纸本)9781479952939;9781479952946

This paper addresses state estimation where domain knowledge is represented by non-linear inequality constraints. To cope with non-Gaussian state distribution caused by the utilisation of domain knowledge, a truncated unscented particle filter method is proposed in this paper. Different from other particle filtering schemes, a truncated unscented Kalman filter is adopted as the importance function for sampling new particles in the proposed truncated unscented particle scheme. Consequently more effective particles are generated and a better state estimation result is then obtained. The advantages of the proposed truncated unscented particle filter algorithm over the state-ofthe-art particle filters are demonstrated through Monte-Carlo simulations.

关键词： Atmospheric measurements Kalman filters Monte Carlo methods Probability density function Roads State estimation Vectors particle filters Kalman filters Monte Carlo technique State estimation atmospheric monitoring inequality constraint Probability density function Road domain knowledge particle filtering algorithm

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Simultaneous Fingerprinting and Mapping for Multimodal Image and WiFi Indoor Positioning 5

Simultaneous Fingerprinting and Mapping for Multimodal Image...

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International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Levchev, Plamen Krishnan, Michael N. Yu, Chaoran Menke, Joseph Zakhor, Avideh Univ Calif Berkeley Dept EECS Berkeley CA 94720 USA

ISBN: (纸本)9781467380546

In this paper, we propose an end-to-end system which can be used to simultaneously generate (a) 3D models and associated 2D floor plans and (b) multiple sensor e.g. WiFi and imagery signature databases for the large scale indoor environments in a fast, automated, scalable way. We demonstrate ways of recovering the position of a user carrying a mobile device equipped with a camera and WiFi sensor in an indoor environment. The acquisition system consists of a man portable backpack of sensors carried by an operator inside buildings walking at normal speeds. The sensor suite consists of laser scanners, cameras and an IMU. particle filtering algorithms are used to recover 2D and 3D path of the operator, a 3D point cloud, the 2D floor plan, and 3D models of the environment. The same walkthrough that produces 2D maps also generates multi-modal sensor databases, in our case WiFi and imagery. The resulting WiFi database is generated much more rapidly than existing systems due to continuous, rather than stop-and-go or crowd-sourced WiFi signature acquisition. We also use particle filtering algorithms in an Android application to combine inertial sensors on the mobile device, with 2D maps and WiFi and image sensor databases to localize the user. Experimental for the second floor of the electrical engineering building at UC Berkeley campus show that our system achieves an average localization error of under 2m.

关键词： Wi-Fi Multimodal Imaging Cameras end system Sensors Floor plans MAPPING(MATHEMATICAL) Mobile devices three-dimensional displays inertia sensor acquisition system images Imagery (Psychotherapy) Database particle filtering algorithm

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Research on VHF/UHF Signal Localization Techniques Based on ITU-R P.1546 Radio Wave Propagation Model 14

Research on VHF/UHF Signal Localization Techniques Based on ...

引用

IEEE International Conference on Computer and Information Technology (CIT 2014)

作者： Wei, Wen Xia, Nan Zhang, Xiaofei Song, Keping State Radio Monitoring Ctr Beijing Peoples R China

ISBN: (纸本)9781479962396

the increasing number of new technologies and new services make the radio wave propagation environment become more complex. This also brings more difficulties to VHF/UHF signal monitoring. The existing VHF/UHF monitoring systems have some problems, such as difficulty of monitoring station site selection, high expense of equipment and maintenance, low receiver sensitivity and multipath effect. To solve the problems above, this paper studies the signal localization techniques based on ITU-R P.1546 radio wave propagation model. With particle filtering algorithm, the longitude and latitude and transmit antenna height of the transmitter could be jointly estimated. The proposed algorithm has been verified by computer simulation and actual measurement data. Compared with the localization results of existing monitoring system, the results of the proposed algorithm are more reliable especially in the case of complex transmission enviorment.

关键词： VHF/UHF radio wave propagation model ITU-R P.1546 particle filtering algorithm localization

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Extension particle filtering algorithm for State and Parameter Estimation in Dynamic Control Process

Extension Particle Filtering Algorithm for State and Paramet...

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2009中国控制与决策会议

作者： GAO Xian-zhong,HOU Zhong-xi,REN Bo-tao College of aerospace and material engineering,National University of Defense Technology,Changsha 410073

Aiming to solve the problem of unknown parameters estimation in nonlinear and/or non-Gaussian dynamic system,Extension particle filtering(EPF) algorithm was *** algorithm modeled unknown parameters by Gaussian random walk process,regarded unknown parameters in dynamic system as a part of state variations,and then estimated the state variations in extension nonlinear dynamic system by particle filtering *** order to improve the estimate precision of unknown parameters by utilizing observable information effectively,a new important density was purposed to instead of Bootstrap filter,further more,it avoided transformation about *** order to solve the problem that covariance augmented infinitely with time in Gaussian random walk model,and Kernel smooth factor restrained covariance excessively so as to the values of estimate parameters could not access the values of true parameters sufficiently,the Gradually Reduce(GR) factor was purposed to instead of Kernel *** the end of this paper,the effectiveness and availability of purposed algorithm was validated by an exemplum.

关键词： particle filtering algorithm Parameters estimation Important density Smooth factor

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