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Improvement in UWB Indoor Positioning by Using Multiple Tags to Filter Positioning Errors

JOURNAL OF INTERNET TECHNOLOGY 2019年第3期20卷 677-688页

作者： Tsai, Ming-Fong Thanh-Nam Pham Hue, Bo-Cai Hsu, Fang-Rong Natl United Univ Dept Elect Engn Taipei Taiwan Feng Chia Univ Dept Informat Engn & Comp Sci Taichung Taiwan Thai Nguyen Univ Dept Elect & Commun Technol Thai Nguyen Vietnam

In this paper, we introduce an indoor-positioning system using ultra-wideband radio signals. To enhance the accuracy of indoor positioning, in our study, we propose to use multiple anchors installed at the same locations to filter positioning error to reduce the instability caused by receiving signals. In addition, we also propose a filtering algorithm referring to the absolute position and moving-direction information of the positioned object and a prediction method to predict the next position of the positioned object based on previous coordinates. When the error values are out of the acceptable range, it can adopt prediction results to conduct calibration using a control object. From the experimental results, our proposed method is effective in enhancing the accuracy of indoor positioning compared to other related works.

关键词： Indoor positioning Ultra-wideband filtering algorithm Automated guided vehicle

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Použití filtrovacích algoritmů ve shlukové analýze

Použití filtrovacích algoritmů ve shlukové analýze

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作者： Pacovský, Matěj Charles University of Prague

The thesis is divided into five chapters. In the first two chapters I give the overview of clustering data analysis, I present definitions of terms used in the work and describe the k-means algorithm. Third chapter focuses on the filtering algorithm that uses heuristics when algorithm pass throught the MRKD-tree. The fourth chapter describes the x-means algorithm that uses all of the above-mentioned findings. In the fifth chapter I test all algorithms both on artificial and real data from physics. In some cases I refer to the WEKA program where the x-means algorithm is implemented. Algoritms that are discussed in this thesis are intended only for objects described by quantitative variables. They are also suitable for large datasets. In the attached CD I present the implementation of algorithms in Matlab language.

关键词： Cluster analysis k-means algorithm x-means algorithm filtering algorithm

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Shape optimization with level set-based method using a reaction diffusion equation for 2D sound barrier

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ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS 2024年 169卷

作者： Yu, Qiuzi Zhang, Fang Matsumoto, Toshiro Chen, Haibo Univ Sci & Technol China Dept Modern Mech CAS Key Lab Mech Behav & Design Mat Hefei 230027 Anhui Peoples R China Nagoya Univ Dept Mech Syst Furo ChoChikusa Ku Nagoya 4648604 Japan

A level set-based method using a reaction diffusion equation is applied for optimization problems of two dimensional (2D) sound barriers. The level set method is employed to implicitly represent the sound barrier structure, which distinguishes the material and void domains by the value of the level set function. The boundary element method is employed to solve acoustic problems governed by Helmholtz equation. Topological derivatives are computed by the boundary integral equation combined with the adjoint variable method. The distribution of level set function is iteratively updated based on the reaction diffusion equation to find the optimal structure. For the existent floating scatterers in the optimization process and the sharp and narrow parts on the surface of the sound barrier, we propose a filtering algorithm to remove floating scatterers and develop a method to achieve a smooth surface of the sound barrier. The shape optimization of sound barriers is achieved using these techniques, integrating the level set-based topology optimization method. Numerical tests are provided to demonstrate the validity and effectiveness of the proposed methods.

关键词： Topology optimization Sound barrier filtering algorithm Boundary element method Level set method

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Applying a Robust Empirical Method for Comparing Repeated LiDAR Data with Different Point Density

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FORESTS 2022年第3期13卷 380页

作者： Viedma, Olga Univ Castilla La Mancha Dept Environm Sci Avda Carlos 3 Toledo 45071 Spain

A key aspect of vegetation monitoring from LiDAR is concerned with the use of comparable data acquired from multitemporal surveys and from different sensors. Accurate digital elevation models (DEMs) to derive vegetation products, are required to make comparisons among repeated LiDAR data. Here, we aimed to apply an improved empirical method based on DEMs of difference, that adjust the ground elevation of a low-density LiDAR dataset to that of a high-density LiDAR one for ensuring credible vegetation changes. The study areas are a collection of six sites over the Sierra de Gredos in Central Spain. The methodology consisted of producing "the best DEM of difference" between low- and high-density LiDAR data (using the classification filter, the interpolation method and the spatial resolution with the lowest vertical error) to generate a local "pseudo-geoid" (i.e., continuous surfaces of elevation differences) that was used to correct raw low-density LiDAR ground points. The vertical error of DEMs was estimated by the 50th percentile (P-50), the normalized median absolute deviation (NMAD) and the root mean square error (RMSE) of elevation differences. In addition, we analyzed the effects of site-properties (elevation, slope, vegetation height and distance to the nearest geoid point) on DEMs accuracy. Finally, we assessed if vegetation height changes were related to the ground elevation differences between low- and high-density LiDAR datasets. Before correction and aggregating by sites, the vertical error of DEMs ranged from 0.02 to -2.09 m (P-50), from 0.39 to 0.85 m (NMDA) and from 0.54 to 2.5 m (RMSE). The segmented-based filter algorithm (CSF) showed the highest error, but there were not significant differences among interpolation methods or spatial resolutions. After correction and aggregating by sites, the vertical error of DEMs dropped significantly: from -0.004 to -0.016 m (P-50), from 0.10 to 0.06 m (NMDA) and from 0.28 to 0.46 m (RMSE);and the CSF filter algorit

关键词： multitemporal LiDAR DEMs of difference filtering algorithm vertical error pseudo-geoid vegetation growth

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Autonomous onboard estimation of mean orbital elements for geostationary electric-propulsion satellites

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AEROSPACE SCIENCE AND TECHNOLOGY 2019年 94卷 105369-000页

作者： Li, Lincheng Zhang, Jingrui Zhao, Shuge Qi, Rui Li, Yanyan Beijing Inst Technol Beijing Peoples R China Beijing Inst Elect Syst Engn Beijing Peoples R China China Aerosp Sci & Ind Corp Beijing Peoples R China

Mean orbital elements estimation for geostationary (GEO) satellites is important for related studies, including station-keeping, rendezvous and end-of-life disposal. With increasingly limited operational slots in GEO region and the advance of all-electric-propulsion satellites, a fast and accurate mean orbital element estimation tool is necessary. In order to balance estimation precision and mission cost as well as to be independent of the ground station, this paper develops an autonomous onboard estimation method of the mean orbital elements for geostationary electric-propulsion satellites. Natural perturbations in GEO, including Earth's triaxiality, luni-solar attractions, and solar radiation pressure, are considered. Terms of appropriate orders due to these effects are chosen to model the semi-analytical dynamics, where modified short-period variations and differential mean orbital elements are derived. Regarding mean orbital elements as state variables and osculating orbital elements as measurements, a filter as well as analytical Jacobians is formulated to make the accurate estimation. Five scenarios are simulated to validate the accuracy and efficiency of the proposed method in the GEO region. (C) 2019 Elsevier Masson SAS. All rights reserved.

关键词： Geostationary orbit Mean orbital elements filtering algorithm Autonomous onboard estimation

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Cramer Rao Lower Bound of Vehicle Target Motion Parameter Estimation with Incomplete Measurement

World Scientific Research Journal

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World Scientific Research Journal 2019年第12期5卷 86-92页

作者： Sujuan Chen

The detection probability of the radar of the vehicle target tracking system is often less than 1 during driving on urban roads, and the measurement data loss problem may occur. In this paper, the stability of the vehicle target tracking system is studied and the sufficient conditions are given for the stability of the mean-square exponent under incomplete measurement conditions. When the probability of detection is known, the Cramer Rao Lower Bound (CRLB) of vehicle target motion parameter estimation is given under the statistical significance of the target tracking system under the condition of incomplete measurement.. The simulation part studies the influence of detection probability on CRLB.

关键词： Detection probability target tracking system incomplete measurement filtering algorithm

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Supervision and performance optimization of rechargeable battery SoC based on lightweight neural network

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JOURNAL OF ENERGY STORAGE 2024年 99卷

作者： Zhong, Chen-Ming Li, Guang-Yao Zheng, Xi Su, Yu-Han Zhou, Bai-Hong Lu, Yi-Jun Chen, Zhong Guo, Wei-Jie Xiamen Univ Sch Elect Sci & Engn Dept Elect Sci Natl Innovat Platform Fus Ind & Educ Integrated Ci Xiamen 361005 Peoples R China Jiangxi Univ Sci & Technol Sch Informat Engn Key Lab Multidimens Intelligent Percept & Control Ganzhou 341000 Peoples R China Chengdu Baiwei Elect Dev Co Ltd Chengdu 611731 Peoples R China

A state-of-charge (SoC) monitoring scheme for rechargeable batteries that can predict and filter the voltage dip time period, effectively reduce abnormal fluctuations in SoC data, and improve battery SoC evaluation stability is proposed. This scheme, which is based on the Kalman filtering algorithm and a lightweight neural network, can be applied to evaluate battery SoCs for wearable and portable devices. This method outperforms the Gaussian naive Bayes (GNB) and the support vector machine (SVM) algorithms in terms of predicting voltage dip time periods, with an accuracy of 95.27 %. The experimental results show that the method can be implemented on real hardware systems and used for SoC evaluation of several kinds of rechargeable batteries, demonstrating the scheme's generality.

关键词： Rechargeable battery State-of-charge (SoC) Neural network filtering algorithm

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Performance Criteria for the Identification of Inertial Sensor Error Models

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SENSORS 2019年第9期19卷 1997.-1997.页

作者： Stepanov, Oleg Motorin, Andrei ITMO Univ JSC CSRI Elektropribor St Petersburg 190000 Russia

This paper considers performance criteria for the identification of sensor error models and the procedure for their calculation. These criteria are used to investigate the efficiency of the identification problem solution, depending on the initial data, and to carry out a comparative analysis of various suboptimal algorithms. The calculation procedure is based on an algorithm that solves the joint problem of hypothesis recognition and parameter estimation within the Bayesian approach. A performance analysis of the models traditionally used to describe errors of inertial sensors is given to illustrate the application of the procedure for the calculation of performance criteria.

关键词： inertial sensor filtering algorithm sensor error model identification Bayesian approach performance criteria

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An Improved Model Equation Based on a Gaussian Function Trinomial for State of Charge Estimation of Lithium-ion Batteries

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ENERGIES 2019年第7期12卷 1366页

作者： Linghu, Jinqing Kang, Longyun Liu, Ming Hu, Bihua Wang, Zefeng South China Univ Technol Sch Elect Power New Energy Res Ctr Guangzhou 510640 Guangdong Peoples R China South China Univ Technol Guangdong Key Lab Clean Energy Technol Sch Elect Power Guangzhou 510640 Guangdong Peoples R China

Establishing a model equation with high accuracy and high computational efficiency is very important for the estimation of battery state of charge (SOC). To ensure better SOC estimation results, most studies have focused on the improvement of the algorithm, while the impact of the model equation which may offset the benefits of advanced algorithms has been overlooked. To address this problem, this paper studies the widely used model equations and presents a new model equation based on a Gaussian function that improves the SOC estimation accuracy and computational efficiency. With the Worldwide harmonized Light Vehicles Test Cycle (WLTC) which is highly dynamic and more realistic than any other driving cycles, the proposed model equation is applied to different filtering algorithms to validate its performance in SOC estimation. The results indicate that the proposed model equation can greatly improve the accuracy of SOC estimation without an increase of computation. In addition, for the traditional polynomial-based model equations, the 6th-order power function polynomial has better performance in SOC estimation than polynomials with other orders.

关键词： lithium-ion battery state of charge model equation Gaussian function trinomial filtering algorithm

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Analysis of a safe utilization algorithm for retired power batteries from new energy vehicles

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HELIYON 2024年第1期10卷 e23994页

作者： Luo, Daobao Han, Jianguo Hu, Xin Henan Polytech Sch Automot & Transportat Zhengzhou 450046 Peoples R China

The graded utilization of waste batteries has gained research significance due to recent reports of new energy vehicle lithium-ion batteries exploding whilst awaiting recycling or in end-of-life storage. In this study, we innovatively selected battery performance parameters such as the internal resistance, charge and discharge rate, and current maximum available capacity to evaluate the safety of retired power batteries from the perspective of inducing thermal runaway. A fractional calculus theory was then introduced, and the fractional second-order resistance as well as a capacitance model and an adaptive genetic algorithm were established for the identification of the parameters. An improved dual-scale filtering algorithm was generated, which combined the extended Kalman filter algorithm and the unscented Kalman filter algorithm to improve the accuracy of the parameter estimation. The final test outcomes indicated that the equivalent circuit model optimized by incorporating multiple filtering algorithms had error rates of 1.87 %, 1.65 %, and 1.27 % for the state of charge of the battery in three different operating condition testbeds, with average errors of 0.62 %, 0.69 %, and 0.59 %, respectively. When an initial experimental platform was constructed for the detection of the parameters, the voltage error quickly stabilized to within 0.03 V. It also displayed many advantages of data detection and calculation, such as faster convergence, faster tracking, and the highest result accuracy when compared with the battery model using other algorithms. This experiment highlighted that a fractional second-order resistive-capacitive circuit equivalent battery state detection model incorporating various filtering algorithms has practicality and feasibility.

关键词： New energy vehicle Lithium -ion battery Genetic algorithm filtering algorithm State detection

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