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Proceedings of the 2022 5th International Conference on Telecommunications and Communication Engineering

作者： Yuhang Tang Wei Liu College of Electronic Science National University of Defense Technology China

ISBN: (纸本)9781450397797

When using beamforming technology, it is often necessary to accurately determine the user's location. Aiming at the problem of low accuracy when using single base station to determine target location in remote areas at present. Based on the kalman filtering algorithm combined with the state estimation fusion method, this paper successfully improves the positioning effect. First, three kinds of motion scenes are designed and established: uniform linear motion scene, uniform acceleration linear scene, and high mobility scene. Then, the prediction equation and observation equation are respectively established for the three scenarios. At this time, two observation equations are established, and then the state estimation fusion method is used to improve the tracking accuracy. The effectiveness of this method is verified by experimental simulation.

关键词： Beamforming State fusion estimation kalman filter algorithm Motion model

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Identification of acceleration harmonic for an electro-hydraulic servo shaking table based on kalman filter

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2013年第8期35卷 986-996页

作者： Yao, Jianjun Di, Duotao Jiang, Guilin Gao, Shuang Yan, Han Harbin Engn Univ Coll Mech & Elect Engn Harbin Heilongjiang Peoples R China

Non-linearities commonly exist in an electro-hydraulic servo shaking table, causing acceleration harmonics distortion when the shaking table is excited by a sinusoidal acceleration signal, because its acceleration response includes higher harmonics, which lower the control performance for an electro-hydraulic servo shaking table. To cancel the harmonics in the system response, thus to improve the shaking table performance, we need to know about the harmonics information. An identification algorithm is developed here based on a kalman filter for dynamically tracking the acceleration harmonics for the electro-hydraulic servo shaking table. A linear system in state space is modelled. The system acceleration response is applied as an observation value and is imported to the kalman filter, which recursively estimates the state vector of the linear system. The amplitude and phase of each harmonic are calculated from the estimated state vector, and their estimated values are validated. A simulation example is presented and experiments were performed on the electro-hydraulic servo shaking table. Both results show a good estimation performance of the proposed acceleration harmonic identification algorithm.

关键词： Acceleration distortion electro-hydraulic servo shaking table harmonic identification kalman filter algorithm

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Alignment of non-texture video frames using kalman filter

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IET COMPUTER VISION 2011年第1期5卷 77-85页

作者： Jianchao, Y. Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Video registration in the presence of several segments of non-texture frames remains a challenging issue, although a wide repertoire of image registration algorithms was developed over the last two decades. In this study, the authors proposed an effective causal scheme of video frames registration, and implemented it by the modified two frames intensity match algorithm. In order to achieve its real-time performance, the authors avoid handling a much more complex appearance model in this causal system by using registration parameters as measurement values, which are estimated directly from two consecutive frames via intensity matching. The simple linear and Gaussian system is thus derived and an efficient kalman filter algorithm can be utilised. As the kalman filter naturally incorporated the temporal information contained in a video into the estimation of registration parameters, the algorithm developed in this study is quite robust and has good performance even if the processed video contains several segments of non-texture frames.

关键词： video registration linear systems kalman filter algorithm Gaussian system image registration linear system Video signal processing filtering methods in signal processing kalman filters Optical, image and video signal processing nontexture video frames video signal processing intensity match algorithm image registration algorithms

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kalman filter-based centralized controller design for non-square multi-input multi-output processes

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2018年 132卷 187-198页

作者： Fasih, Hamid Tavakoli, Saeed Sadeghi, Jafar Torabi, Hamed Univ Sistan & Baluchestan Dept Elect Engn Zahedan Iran Univ Sistan & Baluchestan Dept Chem Engn Zahedan Iran

In this paper, a simple and effective centralized controller is presented to control non-square multi-input multi-output industrial processes with heavy interactions and significant time delays. The control objective is to achieve a good load disturbance rejection and set point tracking. The controller and process model equations are represented in a stochastic state space. Also, the state and measurement equations respectively use general random walk model and finite impulse response model of the plant. To design the controller, a quadratic cost function including the control error vector as well as the input vector and its changes is introduced. A kalman filter algorithm is used to solve the discrete algebraic Riccati equation and to estimate the state vector of the controller recursively. This novel use of kalman filter combines the observer and controller in a block. No square down/up, pairing and pseudo-inversing, is required for the proposed control structure. This leads to the simplicity of controller design. To evaluate the performance of the proposed control strategy, it is applied to a simulation example and an industrial case study with heavy interactions and significant time delays, an atmospheric distillation column with 7 inputs and 4 outputs. Simulation results show that the proposed controller has a good performance in both load disturbance rejection and setpoint tracking. (C) 2018 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Atmospheric distillation column Centralized controller General random walk models kalman filter algorithm Non-square multi-input multi-output process

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Application of kalman filter to hot-spot temperature monitoring in oil-immersed power transformer

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2013年第4期8卷 322-327页

作者： Chen, Weigen Su, Xiaoping Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400044 Peoples R China Chengdu Power Supply Co Chengdu 610017 Peoples R China

Winding hot-spot temperature (HST) is an important factor that affects the insulation life of an oil-immersed power transformer. Thus, precise prediction and close monitoring of HSTs are necessary to avoid thermal damage. In this paper, a differential equation for HST prediction is presented, which takes into consideration the effects of the top-oil temperature variations and thermal dynamics of the load. A discrete form of this equation based on the framework of the kalman filter (KF) algorithm was used to establish a real-time estimation model for the HST. The KF-based model was validated by a sample heat-run test involving a transformer setup in the laboratory. Moreover, the proposed model was applied to a real, large power transformer and compared with the classical IEEE-Annex G method. Results show that the HSTs estimated by the KF-based model are closer to the measured values. The exhibited potential applicability and generality in real-time prediction for HST demonstrate that the proposed model can be employed for online monitoring of HSTs for large power transformers. (c) 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： power transformer hot-spot temperature kalman filter algorithm online monitoring

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A kalman filter method for estimation and prediction of space-time data with an autoregressive structure

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JOURNAL OF STATISTICAL PLANNING AND INFERENCE 2019年 203卷 117-130页

作者： Lagos-Alvarez, Bernardo Padilla, Leonardo Mateu, Jorge Ferreira, Guillermo Univ Concepcion Dept Stat Concepcion Chile Univ Jaume 1 Dept Math Castellon de La Plana Spain

We propose a new kalman filter algorithm to provide a formal statistical analysis of space-time data with an autoregressive structure. The kalman filter technique allows to capture the temporal dependence as well as the spatial correlation structure through state-space equations, and it is aimed to perform statistical inference in terms of both parameter estimation and prediction at unobserved locations. We put in relevance the nugget effect at the observation equation. We test our procedure and compare it with classical kriging prediction via an intensive simulation study. We show that the kalman filter is superior in both the estimation, without using a plug-in approach, and prediction for spatio-temporal data, providing a suitable formal procedure for the statistical analysis of space-time data. Finally, an application to the prediction of daily air temperature data in some regions of southern Chile is presented. (C) 2019 Elsevier B.V. All rights reserved.

关键词： kalman filter algorithm Simple kriging Space-time geostatistics Space-time models State-space system

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Denoising by Local Iterated Extended kalman filter for Quantification Purposes in Electronic Noses 14

Denoising by Local Iterated Extended Kalman Filter for Quant...

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International Symposium on Communications and Information Technologies

作者： Turmchokkasam, Sirichai Chamnongthai, Kosin King Mongkuts Univ Technol Thonburi Fac Engn Dept Elect & Telecommunicat Engn Bangkok Thailand Bangkok Univ Dept Elect & Elect Engn Bangkok Thailand

ISBN: (纸本)9781479944163

In the area of electronic noses (e-noses), applications in the field of wine detection are uncommon. The number of qualified human wine experts is low and their cost is high. This paper has been developed for the purpose of local iterated extended kalman filtering technique for collecting raw data (typical red wine aromas) and de-noising based on the output noise characteristics of those gas sensors. Compared to the conventional kalman filter algorithm.

关键词： Wine Aromas Electronic Noses Local Iterated Extended kalman filtering Technique kalman filter algorithm

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kalman filter and Particle Swarm Optimization on Real Time Satellite Tracking

Kalman Filter and Particle Swarm Optimization on Real Time S...

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CIE International Conference on Radar (RADAR)

作者： Kocadag, Fatih Cinar, Ridvan Firat Demirkol, Askin Abant Izzet Baysal Univ IT Ctr Bolu Turkey Sakarya Univ Elect & Elect Engn Fac Sakarya Turkey

ISBN: (纸本)9781509048281

In this study, it is aimed to tracking a satellite on mobile vehicles for receiving the broadcasting signal of the satellite. Satellite tracking is used in many areas on mobile platforms. Too many problems come with the mobility. A robust tracking algorithm is need for overcome such problems. The robust satellite tracking case is researched and test software is developed for Android based platforms. Satellite tracking can be done with a receiver antenna by adjusting the positions according to the movements of the vehicle. Therefore kalman filter and Particle Swarm Optimization algorithm are compared in the use of satellite tracking process. An Android based application was developed to accomplish this and tests were performed using an android - based device on a mobile vehicle. Test data were collected through application from the sensors of the android device. Orientation and location data of the mobile vehicle are collected from the device sensors and they are used in computation of the azimuth and elevation angles of receiver antenna. Two well-known algorithms were implemented and performed on the application for tracking to keep satellite link online. It was systemically investigated the estimation and computational performance of the two algorithm on this subject. The results and test cases were analyzed and discussed. Test cases cover the computation of the azimuth and elevation angles.

关键词： kalman filter algorithm Particle Swarm Optimization algorithm target tracking azimuth elevation and skew angle formula accelerometer magnetometer GPS data reading

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Front Moving Vehicle Detection and Tracking with kalman filter 4

Front Moving Vehicle Detection and Tracking with Kalman Filt...

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4th IEEE International Conference on Computer and Communication Systems (ICCCS)

作者： Rin, Vannat Nuthong, Chaiwat King Mongkuts Inst Technol Ladkrabang Int Coll Bangkok Thailand

ISBN: (纸本)9781728113227

Immature behavior, fatigue, and inattentional driving have been mainly identified as major issues in safety. Such human behaviors are considered to be the core problem for the occurrences of traffic accidents. As a result, Intelligent Transportation System including Safety Driver Assistance Systems plays an important role in traffic safety. This system becomes the focused research topics recently. This paper aims to make an investigation of front moving vehicles detection and tracking for the forward collision warning system. In the proposed system, there are two major parts, i.e. vehicle detection and vehicle tracking for position prediction. For the detection procedure, this paper makes use of computer vision techniques with shifting three frame difference method on video sequence and combines with edge detection. For object's boundary selection, this work applies the blob-detection to extract the center of mass and the bounding box features of the detected object. After having selected those features of the front vehicle the tracking process is carried on by utilizing kalman filter algorithm in order to improve the accuracy of object detection and position prediction. Three videos obtained on the urban structured road in Thailand are tested. Experimental results show that the detection rate of front moving vehicle performs better when kalman filter is applied for tracking.

关键词： front object detection object tracking kalman filter algorithm

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Fast Estimation Method for Selection of Optimal Distributed Generation Size Using kalman filter and Graph Theory 17

Fast Estimation Method for Selection of Optimal Distributed ...

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17th IEEE UKSim-AMSS International Conference on Computer Modelling and Simulation (UKSim)

作者： Al Ameri, Ahmed Nichita, Cristian Abbood, Hilal Al Atabi, Ali Univ Le Havre GREAH Lab Grp Rech Electrotech & Automat Le Havre France Univ Liverpool Elect Engn & Elect Dept Liverpool Merseyside England

ISBN: (纸本)9781479987122

This paper presents a linearised model for a Distributed Generation (DG) network with the aim of estimating optimal size of DG using kalman filter and graph theory. Relationships between the active power and voltage angle and between the reactive power and voltage magnitude are utilised to determine the minimum real power loss and voltage profile of the distributed network. A 2-stage method is suggested to estimate the best size of DGs. In the first stage, the graph flow method is used to generate the incident matrix to build the linear model and in the second stage, a kalman filter algorithm is applied to obtain the optimal size of the DG at each bus system. The proposed method has been implemented and tested on standard IEEE 5-bus test system. Simulation results showed that the proposed method is of a significant time saving when compared to an existing method based on load flow.

关键词： distributed generation kalman filter algorithm graph theory power system analysis linear load flow

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