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2024 8th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Wei Li Jianjin Zou Li Ma Zhan Shi School of Information Communication Chinese people’s Liberation Army National University of Defense Technology Wuhan China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

To improve the positioning accuracy of a pedestrian inertial navigation system (INS), an INS constraint algorithm based on an adaptive zero-velocity update algorithm (ZUPT) and heading angle error correction was proposed. First, the threshold of zero-velocity detection based on the generalised likelihood ratio and eigenvalue of the test statistic are used to establish an equation and adaptive adjustment of the threshold of zero-velocity detection. Subsequently, a linear regression model was established based on the solution position of the INS. Combining the heading difference between the main heading and solved heading, the observation vector and observation matrix are constructed according to the pedestrian movement type to correct the INS error. Ultimately, the experimental validation confirmed that when pedestrians move in a complex route with variable velocity, compared with the zero-velocity detection algorithm with a fixed threshold and the main course correction algorithm, the proposed algorithm is closer to the real trajectory, and the average closed-loop error is reduced from $3.31 \% \mathrm{D}$ to $1.45 \%$ D, which effectively improves the positioning accuracy of the INS and has good engineering application value.

关键词： Pedestrians Accuracy Linear regression Inertial navigation filtering algorithms Mathematical models Vectors Error correction Trajectory Kalman filters

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Finding Errors for Determining the Parameters of Movement of Maneuvering Objects

Finding Errors for Determining the Parameters of Movement of...

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Microwave Conference (RMC), All-Russian

作者： Vladimir Mikhaylovich Artyushenko Vladimir Ivanovich Volovach Information technology and management systems department Technological University Korolev city Russian Federation Informational and electronic service department Volga Region State University of Service Togliatty city Russian Federation Voronezh State University Voronezh city Russian Federation

ISBN: (数字)9798331540944

ISBN: (纸本)9798331540951

The analysis of the influence of multiplicative noise acting simultaneously with additive noise on the accuracy of measurement of arrival time of the useful signal by the tracking meter is carried out. The input signal is given as a sequence of coherent and incoherent pulses. It is shown that a very low level of additive noise leads to the fact that multiplicative noise does not affect the steepness of the discriminatory characteristic. It is noted that an increase in the spectrum width of the noise modulation function leads to an increase in measurement errors due to a decrease in the power of the input signal. However, the power reduction can be compensated by the effect of automatic gain control. The value of the accuracy characteristics of the tracking meter under the influence of multiplicative noise is determined by the steepness of the discriminatory characteristic, the coefficients determining the normalizing effect of automatic gain control in the presence and absence of noise, as well as the spectral density of fluctuations at the discriminator output.

关键词： Meters Additive noise Maximum likelihood detection Accuracy Nonlinear filters filtering algorithms Time measurement Gain control Noise measurement Microwave filters

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Robust Augmented Diffusion Active Noise Control Algorithm Based on Generalized Maximum Correntropy

Robust Augmented Diffusion Active Noise Control Algorithm Ba...

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IEEE International Conference on Electronics and Communication Engineering (ICECE)

作者： Yang Zhou Haiquan Zhao Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle School of Electrical Engineering Southwest Jiaotong University Chengdu China

ISBN: (数字)9798331517755

ISBN: (纸本)9798331517762

Multichannel active noise control (ANC) is a noise cancellation technology designed to effectively eliminate low-frequency noise, thereby creating a large quiet zone in a given space. Distributed ANC systems based on diffusion strategies have garnered significant attention due to their low computational complexity and flexible scalability. However, recent related works have largely overlooked the interference of impulsive noise on these systems, developing algorithms solely based on the mean square error (MSE) criterion. This paper addresses this gap by employing the generalized maximum correntropy criterion (GMCC) and introducing an augmented weight vector for the control filter. Consequently, we develop an augmented diffusion filtered-x GMCC (ADFxGMCC) algorithm. Finally, computer simulations validate the robust noise reduction performance of the proposed algorithm in asymmetric ANC systems under impulsive noise interference.

关键词： Wireless sensor networks Scalability Noise Noise reduction Interference filtering algorithms Noise cancellation Vectors Acoustics Topology

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Tracking Differentiator-based Multiview Dilated Characteristics for Time Series Classification

Tracking Differentiator-based Multiview Dilated Characterist...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Changchun He Xin Huo Control and Simulation Center Harbin Institute of Technology Harbin China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

In industrial production, obtaining the discrete status of equipment from massive time series data has become an urgent demand, which places higher requirements on the time series classification (TSC) algorithm. The feature-based TSC method achieves interpretable and potential classification capability by extracting meaningful descriptive statistical features. Limitations exist in current feature-based TSC algorithms, hindering the acquisition of diverse statistical features due to a lack of knowledge and insufficient research on processing redundant features, thereby limiting performance enhancements. Therefore, in this article, a novel feature-based TSC algorithm tracking differentiator-based multiview dilated characteristics (TD-MVDC) is proposed. The innovative introduction of a tracking differentiator combined with dilation mapping as a preprocessor into the feature-based TSC method is proposed to improve feature diversity efficiently. Ensemble feature selection based on filter feature selectors with different store ratios is designed to generate multiview features to enhance feature stability quickly. Linear classifiers and hard voting to fastly classify and integrate multiview features to increase classification performance robustly. Finally, comparative and ablative experiments are conducted between TD-MVDC and the representative feature-based TSC algorithms on the extensively compared UCR archive to verify the effectiveness of the proposed algorithm.

关键词： Limiting Time series analysis Noise Production filtering algorithms Feature extraction Classification algorithms Informatics

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IIR Filter Design Using Sparrow Search Algorithm

IIR Filter Design Using Sparrow Search Algorithm

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International Conference on Computing and Networking Technology (ICCNT)

作者： Shaivi Dewangan Shubham Yadav Suman Kumar Saha Department of ECE National Institute of Technology Raipur Chhattisgarh India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper introduces an advanced methodology for the design of an infinite impulse response (IIR) of order 8, utilizing the Sparrow Search Algorithm (SSA). SSA is a cuttingedge meta-heuristic optimization technique inspired by the foraging and anti-predation behaviors of sparrows, renowned for its swift convergence and precision in identifying optimal solutions. This study harnesses SSA to optimize critical filter parameters, including peak stopband attenuation, transition width and normalized passband attenuation. The optimized coefficients derived through SSA are employed to develop a high-performance IIR filter. Comparative analysis and experimental results underscore the superior efficacy of the SSA-based design approach over traditional optimization techniques, demonstrating notable improvements in accuracy and efficiency. This work underscores the potential of SSA as a formidable tool for the sophisticated designing of IIR filters, offering significant advancements in the field of digital signal processing.

关键词： Design methodology Metaheuristics Signal processing algorithms IIR filters Digital signal processing filtering algorithms Attenuation Reliability Passband Convergence

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Underwater Image Enhancement and Object Recognition Using CNN Algorithm

Underwater Image Enhancement and Object Recognition Using CN...

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Smart Technologies for Sustainable Development Goals (ICSTSDG), International Conference on

作者： Vijayalakshmi. P Seetharaman. M Praveen. E Department of Electronic and Communication Engineering Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Tamil Nadu India

ISBN: (数字)9798331516284

ISBN: (纸本)9798331516291

The research aims to improve underwater image quality due to the scattering and absorption of light, resulting in poor image quality and reduced visibility. In this study, we propose a novel approach for underwater image enhancement and object recognition using Convolutional Neural Network, or CNN algorithms with the integration of a median filter, implemented in MATLAB. The median filter serves as a preprocessing step to reduce noise and enhance image clarity, while the CNN algorithm is employed for object recognition in the enhanced images. Experimental results demonstrate significant improvements in image quality and object recognition accuracy compared to existing methods. The proposed framework offers a promising solution for underwater applications such as marine biology, underwater surveillance, and underwater robotics, where clear imaging and accurate object detection are essential.

关键词： Image quality Photography Accuracy Image color analysis Lighting Object detection filtering algorithms Object recognition Convolutional neural networks Image enhancement

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Sensorless Control of AC Motor Drives with Adaptive Extended Kalman Filter

Sensorless Control of AC Motor Drives with Adaptive Extended...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Saverio Rigon Riccardo Antonello Paolo Mercorelli Mauro Zigliotto Department of Management and Engineering Università Degli Studi di Padova Vicenza Italy Institute for Production Technology and Systems Leuphana University of Lüneburg Lüneburg Germany

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

One of the main difficulties faced in commissioning a sensorless AC motor drive based on an Extended Kalman Filter (EKF) still remains the fine tuning of the noise covariance matrices of the model used for the state estimation. An inadequate choice penalizes the overall system performance, and is the most common cause of the filter divergence. Several manual or automated tuning methods exist in literature, but they often involve time consuming procedures, and are ineffective in dealing with variations of the system dynamics. Aimed to overcome these limitations, in this paper we investigate on the advantages of using an Adaptive Extended Kalman Filter (AEKF) algorithm based on Maximum Likelihood Estimation (MLE) for the simultaneous estimation of the system state and the unknown noise covariance matrices. By continuously adjusting the filter parameters to the actual operating conditions, the proposed system demonstrates greater robustness and outperforms conventional solutions relying on manually tuned EKFs.

关键词： Maximum likelihood estimation Adaptive systems System dynamics Noise Sensorless control filtering algorithms Robustness Kalman filters Covariance matrices Tuning

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A GPS Receiver Adaptive Digital Beamforming Interference Suppression Algorithm Based on Kalman Filter

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Chinese Journal of Electronics 2023年第2期22卷 433-436页

作者： Wenrui Ding Yunlong Zhu Bo Zhang Dongkai Yang Institute of Unmanned Air Vehicle Beihang University Beijing China School of Mechanical Engineering and Automation Beihang University Beijing China School of Electronic and Information Engineering Beihang University Beijing China

This paper proposed a novel Global positioning system (GPS) receiver adaptive Digital beamforming (DBF) interference suppression algorithm, which remodeled Minimum power distortionless response (MPDR) beamforming by transforming it into Minimum mean square error (MMSE) beamforming in the frame of Generalized sidelobe canceller (GSC), and introduced Kalman filter (KF) to carry out adaptive beamforming. Theory analyses and simulation results indicate that this algorithm has higher output Signal to interference and noise ratio (SINR) than Recursive least square (RLS) and Least mean square (LMS) adaptive beamforming algorithms which are in common use, and similar convergence rate with RLS algorithm.

关键词： Interference suppression Adaptive systems Array signal processing Simulation Receivers Transforms filtering algorithms

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Attack Detection and Data Restoration of Remote Estimation Systems Based on D-RCGAN

Attack Detection and Data Restoration of Remote Estimation S...

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Asian Control Conference

作者： Xinyi Zhang Tiejun Wu Ya Zhang School of Automation Southeast University Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

This paper explores the problem of secure estimation in Cyber-Physical Systems (CPSs) under various network attacks. It proposes an attack detection and data restoration method based on the designed Dual-Constraint Recurrent Conditional Generative Adversarial Network (D-RCGAN). This method utilizes the capability of RCGAN to generate real-valued multidimensional time series by extracting temporal features from conditional data. In addition, to address the issue of data generation accuracy, a dual-constraint approach is proposed to optimize the network parameters of RCGAN. For attack detection, a ratio detector is designed to compare the ratio relationship between generated data and real measurements, enabling the determination of whether the measurements are affected by network attacks. In cases where attacks are detected, the generated data is employed to replace the real data for output, facilitating data restoration. Ultimately, optimal state estimation of CPSs is achieved based on the Kalman filter. Simulation results demonstrate that, compared to other generation models, this algorithm offers better precision in attack detection and efficient data restoration.

关键词： Simulation Time series analysis Detectors filtering algorithms Feature extraction Generative adversarial networks Data models

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Robust Extended Kalman Filter for Nonlinear Singular System

Robust Extended Kalman Filter for Nonlinear Singular System

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Chinese Automation Congress (CAC)

作者： Kexin Mu Chenjian Ran Electronic Engineering College Heilongjiang University Harbin China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In this paper, for the stochastic singular systems with nonlinear measurement, a robust extended Kalman filtering algorithm is proposed. The original singular system is transformed into a standard system through the application of the Singular Value Decomposition (SVD) method and the state augmented approach. The nonlinear measurement is handled using the Taylor series expansion method, where the higher-order terms of the linearization error are represented by norm-bounded uncertainties. Further, a time-varying filter is designed by defining a new augmented system and introducing the variance constraint method, which ensures the upper bound of the filtering error covariance. Finally, to demonstrate the effectiveness of the proposed filtering algorithm, a simulation example is provided.

关键词： Upper bound Uncertainty filtering Stochastic processes filtering algorithms Taylor series Kalman filters State estimation Time-varying systems Standards

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