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Application of fuzzy logic control theory combined with target tracking algorithm in unmanned aerial vehicle target tracking

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SCIENTIFIC REPORTS 2024年第1期14卷 1-17页

作者： Li, Cong Zhao, Wenyi Zhao, Liuxue Ju, Li Zhang, Hongyu State Grid Beijing Econ Res Inst Beijing 100055 Peoples R China State Grid Beijing Elect Power Co Beijing 100031 Peoples R China

This paper aims to increase the Unmanned Aerial Vehicle's (UAV) capacity for target tracking. First, a control model based on fuzzy logic is created, which modifies the UAV's flight attitude in response to the target's motion status and changes in the surrounding environment. Then, an edge computing-based target tracking framework is created. By deploying edge devices around the UAV, the calculation of target recognition and position prediction is transferred from the central processing unit to the edge nodes. Finally, the latest Vision Transformer model is adopted for target recognition, the image is divided into uniform blocks, and then the attention mechanism is used to capture the relationship between different blocks to realize real-time image analysis. To anticipate the position, the particle filter algorithm is used with historical data and sensor inputs to produce a high-precision estimate of the target position. The experimental results in different scenes show that the average target capture time of the algorithm based on fuzzy logic control is shortened by 20% compared with the traditional proportional-integral-derivative (PID) method, from 5.2 s of the traditional PID to 4.2 s. The average tracking error is reduced by 15%, from 0.8 m of traditional PID to 0.68 m. Meanwhile, in the case of environmental change and target motion change, this algorithm shows better robustness, and the fluctuation range of tracking error is only half of that of traditional PID. This shows that the fuzzy logic control theory is successfully applied to the UAV target tracking field, which proves the effectiveness of this method in improving the target tracking performance.

关键词： Unmanned aerial vehicle target tracking Edge computing Fuzzy logic control Vision transformer model particle filter algorithm

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A Nonlinear Suboptimal Fusion algorithm Weighted by Matrices Based on LMI and NARX

A Nonlinear Suboptimal Fusion Algorithm Weighted by Matrices...

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第43届中国控制会议

作者： Hangyan Guo Gang Hao Electronic Engineering Institute Heilongjiang University Key Laboratory of Information Fusion Estimation and Detection

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In order to solve the fusion estimation problem of multi-sensor with unknown cross-covariance,an improved suboptimal fusion algorithm weighted by matrices is proposed for nonlinear ***,for significance of linear minimum variance,the simplest constraints based on fusion weighted by matrices are derived by Shure complement *** constraints can ensure the positive definiteness of the fusion estimate error covariance,and the consistency of the proposed suboptimal fusion ***,a suboptimal fusion estimation weighted by matrices is proposed based on linear matrix inequality(LMI).Considering the time-consuming problem in the optimization process of LMI algorithm and the complexity of the nonlinear system,the optimal value is obtained by the nonlinear auto-regressive neural network with exogenous input(NARX).Finally,a nonlinear suboptimal fusion algorithm weighted by matrices based on LMI and NARX is proposed in combination with the particle filter algorithm(PF).

关键词： Suboptimal fusion algorithm weighted by matrices linear matrix inequality particle filter algorithm nonlinear auto-regressive neural network with exogenous input(NARX)

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In-process stochastic tool wear identification and its application to the improved cutting force modeling of micro milling

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2022年 164卷 108233-108233页

作者： Zhang, Xuewei Yu, Tianbiao Xu, Pengfei Zhao, Ji Northeastern Univ Sch Mech Engn & Automat Shenyang Peoples R China

Micro milling aims to manufacture miniature structures with high quality and complex features, and the stochastic time-varying tool wear is a crucial factor which has great influence on machining quality and efficiency of micro milling process. To improve the precision of machining and sustainability of micro cutting tools, the in-process tool wear conditions should be identified and updated ahead of time. In this work, an improved integrated estimation method is proposed based on the long short-term memory (LSTM) network and particle filter (PF) algorithm to predict the stochastic tool wear values. The integrated PF-LSTM identification methodology is developed to predict the in-process stochastic tool wear progression on the basis of the historical measurement data. With the estimation of in-process stochastic tool wear, the cutting force model is modified, in which the influence of tool run-out and the trochoidal trajectory of cutting edge are also considered. The proposed integrated estimation method of in-process stochastic tool wear and the modified cutting force model were validated by the micro milling experiments with workpiece material Al6061. It can be seen from the comparison results that the availability and sustainability of micro cutting tool have been improved, and the prediction accuracy also could be increased by 3.4% compared with that without considering the influence of tool wear.

关键词： Micro milling In-process stochastic tool wear Long short-term memory network particle filter algorithm Cutting forces

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Option pricing under stock market cycles with jump risks: evidence from the S&P 500 index

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REVIEW OF QUANTITATIVE FINANCE AND ACCOUNTING 2021年第1期56卷 25-51页

作者： Wang, Shin-Yun Chuang, Ming-Che Lin, Shih-Kuei Shyu, So-De Natl Dong Hwa Univ Dept Finance Hualien Taiwan Feng Chia Univ Dept Finance Taichung Taiwan Natl Chengchi Univ Coll Commerce Dept Money & Banking 64Sec 2ZhiNan Rd Taipei 11605 Taiwan Natl Chengchi Univ Coll Commerce Risk & Insurance Res Ctr 64Sec 2ZhiNan Rd Taipei 11605 Taiwan Takming Univ Sci & Technol Dept Accounting Informat Taipei Taiwan

This study incorporates the Markov switching model with return jumps to depict the behavior of stock returns. Based on the daily Standard & Poor's 500 index (hereafter SPX) and the daily closing price of the call option, we use the particle filtering algorithm to fit the parameter of the model. The joint log-likelihood evaluates the model performance: the weighted average log-likelihood with the rate of return of the SPX and the relative implied volatility root-mean-squared error for the SPX call options. The empirical results identify that the pricing model with jump risks improves the pricing performance to the median-term call options. According to the sensitivity analysis, option prices increase with the probability of remaining in the recession state but decrease with the probability of remaining in the expansion state. Moreover, the call option prices are positively associated with the volatility in each market state and the factors of jump risk.

关键词： Markov switching Jump risks Volatility smile particle filter algorithm

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Study of SOH Estimation of EMU Battery Based on Improved particle filter

Study of SOH Estimation of EMU Battery Based on Improved Par...

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第40届中国控制会议

作者： Buzhao Niu Liwei Li Chao Xu Fei Yu Department of Quality Management CRRC Qingdao Sifang Co. Ltd. Weihai Innovation Institute Qingdao University School of Electrical Engineering Qingdao University China Railway Guangzhou Group Co. Ltd.Guangzhou EMU depot

With lithium-ion batteries are more and more widely used in transportation, the estimation of battery state-of-health（SOH） is of great significance in the safe and reliable operation of battery management system and the reduction of maintenance cost. Based on the analysis of the traditional particle filter algorithm, the genetic factor of genetic algorithm（GA）is introduced into the particle filter and improved by adaptive mutation. In order to predict the SOH of lithium-ion battery, the health index（HI） is extracted from the measurable parameters of lithium-ion battery. The mapping model between HI index and SOH is established and applied to the observation of state space model. In this paper, a battery SOH estimation method based on improved particle filter algorithm is proposed. The experimental results show that the proposed method is superior to the traditional particle filter（PF） algorithm and has good accuracy in estimating the degradation process of lithium-ion batteries.

关键词： particle battery SOH estimation genetic algorithm particle filter algorithm health indicator

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A Novel Shilling Attack Detection Model Based on particle filter and Gravitation

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China Communications 2019年第10期16卷 112-132页

作者： Lingtao Qi Haiping Huang Feng Li Reza Malekian Ruchuan Wang College of Computer Nanjing University of Posts and TelecommunicationsNanjing 210003China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing 210003China “Broadband Wireless Communication and Sensor Network Technology”Key Lab of Ministry of Education NUPTNanjing 210003China College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 210016China Department of Computer Science and Media Technology Malmo UniversityMalmo 20506Sweden

With the rapid development of e-commerce, the security issues of collaborative filtering recommender systems have been widely investigated. Malicious users can benefit from injecting a great quantities of fake profiles into recommender systems to manipulate recommendation results. As one of the most important attack methods in recommender systems, the shilling attack has been paid considerable attention, especially to its model and the way to detect it. Among them, the loose version of Group Shilling Attack Generation algorithm (GSAGenl) has outstanding performance. It can be immune to some PCC (Pearson Correlation Coefficient)-based detectors due to the nature of anti-Pearson correlation. In order to overcome the vulnerabilities caused by GSAGenl, a gravitation-based detection model (GBDM) is presented, integrated with a sophisticated gravitational detector and a decider. And meanwhile two new basic attributes and a particle filter algorithm are used for tracking prediction. And then, whether an attack occurs can be judged according to the law of universal gravitation in decision-making. The detection performances of GBDM, HHT-SVM, UnRAP, AP-UnRAP Semi-SAD,SVM-TIA and PCA-P are compared and evaluated. And simulation results show the effectiveness and availability of GBDM.

关键词： shilling attack detection model collaborative filtering recommender systems gravitation-based detection model particle filter algorithm

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GNSS multipath estimation and mitigation based on particle filter

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IET RADAR SONAR AND NAVIGATION 2019年第9期13卷 1588-1596页

作者： Qin, Honglei Xue, Xia Yang, Qian Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China

Multipath interference is the main error source for high-precision positioning applications, and multipath suppression and mitigation is particularly important. particle filters are widely used to solve non-linear filtering problems without limitation of Gaussian distribution, and Global navigation satellite system (GNSS) multipath estimation and mitigation based on particle filter are proposed in this study. This approach has four innovations: Firstly, the Kalman-based multipath signal model is improved to obtain a particle filter multipath signal model, and I and Q two channel signals in the algorithm model solve the coherence correlation phase estimation problem. Secondly, the particle filter algorithm constructs the correlation function using the current particle amplitude and delay. Thirdly, taking full advantage of every particle information, the weighted average is used to calculate state quantity of multipath. Fourthly, three resampling algorithms, including simple random, pseudo-parallel genetic algorithm and niche genetic algorithm, are taken to resample. particle filter algorithm for multipath estimation and mitigation based on simulated data and actual navigation satellite signal data is verified. The simulation results show the proposed algorithm has high accuracy in multipath estimation than MEDLL and TK-MEDLL, especially in multichannel multipath estimation. The actual experimental results show particle filter algorithm is effective for improving the positioning accuracy in complex environments.

关键词： phase estimation Kalman filters interference suppression satellite navigation filtering theory radio receivers genetic algorithms multipath channels Gaussian distribution particle filtering (numerical methods) time-of-arrival estimation high-precision positioning applications multipath suppression nonlinear filtering problems Global navigation satellite system multipath estimation Kalman-based multipath signal model coherence correlation phase estimation problem particle filter algorithm current particle amplitude particle information actual navigation satellite signal data multichannel multipath estimation GNSS multipath estimation multipath interference

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Positioning optimisation based on particle quality prediction in wireless sensor networks

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IET NETWORKS 2019年第2期8卷 107-113页

作者： Zhang, Chunjiong Xie, Tao Yang, Kai Ma, Hui Xie, Yuxia Xu, Yueyao Luo, Pan Tongji Univ Coll Elect & Informat Engn 4800 Caoan Highway Shanghai 4800 Peoples R China Southwest Univ Coll Comp & Informat Sci 2 Tiansheng Rd Chongqing Peoples R China

The particle degradation problem of particle filter (PF) algorithm caused by reduction of particle weights significantly influences the positioning accuracy of target nodes in wireless sensor networks. This study presents a predictor to obtain the particle swarm of high quality by calculating non-linear variations of ranging between particles and flags and modifying the reference distribution function. To this end, probability variations of distances between particles and star flags are calculated and the maximum inclusive distance using the maximum probability of high-quality particle swarm is obtained. The quality of particles is valued by the Euclidean distance between the predicted and real observations, and hereafter particles of high quality are contained in spherical coordinate system using the distance as diameter. The simulation results show that the proposed algorithm is robust and the computational complexity is low. The method can effectively improve the positioning accuracy and reduce the positioning error of target nodes.

关键词： particle swarm optimisation particle filtering (numerical methods) wireless sensor networks probability nonlinear variations reference distribution function star flags maximum inclusive distance maximum probability high-quality particle swarm Euclidean distance positioning accuracy target nodes positioning optimisation particle quality prediction wireless sensor networks particle degradation problem particle filter algorithm positioning error reduction particle weights reduction computational complexity

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Research on the kinematic parameter calibration method of industrial robot based on LM and PF algorithm 32

Research on the kinematic parameter calibration method of in...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Deng, Xin Ge, Lianzheng Li, Ruifeng Liu, Zhiheng Harbin Inst Technol Harbin 150001 Peoples R China

ISBN: (纸本)9781728158556

The absolute positioning accuracy of the industrial robot is one of its important performance indexes, which is impacted by the key factor of robotic kinematic parameters. Therefore, based on the MDH model a calibration method of robot kinematic parameters, which combines the Levenberg-Marquardt algorithm with the particle filter algorithm is proposed. Firstly, the MDH model of an industrial robot is established, and the parameters in the tool coordinate transformation are also regarded as the parameters to be calibrated. Then the end error model is established. Secondly, the initial optimization is carried out using the Levenberg-Marquardt (LM) algorithm. Meanwhile, the particle filter algorithm is used to further optimize the parameters considering the measurement error. Finally, compared with other methods, such as spatial circle fitting method, least square method and extended Kalman filter algorithm. The results show that the kinematic parameters of the robot are accurately calibrated and the absolute positioning accuracy of the industrial robot is significantly improved by this method. Compared with other methods, the parameters calibrated by this method have stronger generalization ability.

关键词： Kinematic parameters MDH model Levenberg-Marquardt algorithm particle filter algorithm

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The use of Multiwall model for reference RSSI data preparation for indoor localization purposes

The use of Multiwall model for reference RSSI data preparati...

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2020 Baltic URSI Symposium, URSI 2020

作者： Kawecki, Robert Korbel, Piotr Hausman, Slawomir Institute of Electronics Lodz University of Technology Lodz Poland

ISBN: (纸本)9788394942199

The article discusses the suitability of the multiwall radio wave propagation model for RSSI reference data preparation for fingerprinting-based indoor positioning applications. Localization system employs Bluetooth Low Energy beacons and mobile device to determine position of the user within the test area. The proposed system uses particle filter algorithm to estimate the user's current position from RSSI measurements and knowledge of reference electromagnetic field distribution computed for each beacon located in the building. The results of the comparison between the use of multiwall and ray tracing propagation models for indoor localization purposes has been shown. The relation between the time needed to prepare mentioned reference data was also demonstrated. Presented results show that for some cases it is possible to use less complex propagation models without losing sufficient localization accuracy but for the benefit of the time and complexity of calculations needed to prepare reference data for presented indoor localization application. © 2020 Warsaw University of Technology.

关键词： indoor positioning multiwall model particle filter algorithm received signal strength

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