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检索条件"主题词=filtering algorithms"

共 10331 条记录，以下是321-330 订阅

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Robust Attitude Estimation With Quaternion Left-Invariant EKF and Noise Covariance Tuning

Robust Attitude Estimation With Quaternion Left-Invariant EK...

引用

International Conference on Computer and Automation Engineering, ICCAE

作者： Yash Pandey Rahul Bhattacharyya Yatindra Nath Singh Department of Electronics Engineering Harcourt Butler Technical University Kanpur India Department of Electrical Engineering Indian Institute of Technology Kanpur Kanpur India

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

Accurate estimation of noise parameters is critical for optimal filter performance, especially in systems where true noise parameter values are unknown or time-varying. This article presents a quaternion left-invariant extended Kalman filter (LI-EKF) for attitude estimation, integrated with an adaptive noise covariance estimation algorithm. By employing an iterative expectation-maximization (EM) approach, the filter can effectively estimate both process and measurement noise covariances. Extensive simulations demonstrate the superiority of the proposed method in terms of attitude estimation accuracy and robustness to initial parameter misspecification. The adaptive LI-EKF's ability to adapt to time-varying noise characteristics makes it a promising solution for various applications requiring reliable attitude estimation, such as aerospace, robotics, and autonomous systems.

关键词： Accuracy filtering Quaternions Noise Estimation filtering algorithms filtering theory Kalman filters Time-varying systems Tuning

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System Identification for Yaw Dynamics Prediction Model of an Autonomous Underwater Glider: Comparison of N4SID and ARX Models Using Preprocessed Data

System Identification for Yaw Dynamics Prediction Model of a...

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Advancement in Data Science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Yansha Ramadhan Pratama Muhammad Hanif Hibatullah Bambang Riyanto Trilaksono Egi Hidayat Simon Siregar Yansen Dwiputra School of Electrical Engineering Bandung Institute of Technology Bandung Indonesia Faculty of Applied Science Telkom University Bandung Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

This paper aims to compare two algorithms of system identification between Auto-Regressive with eXogenous inputs (ARX) and Numerical Subspace State Space System Identification (N4SID). This comparison is based on the development of a Model Predictive Control (MPC) for heading control of an Autonomous Underwater Glider (AUG). The comparison was done by compared best-fit values of those models, made from data through a noise filtering process. The filtering process is using a Maximum Likelihood-based Kalman Filter (ML-based KF). ML is used to get the optimal parameters of Q and R value for K $F$ , thus that the optimal value for both parameters can be achieved. Both models have good accuracy performance on best-fit percentage and have low errors. Nevertheless, the ARX model demonstrates a bit advantage over N4SID, achieving the highest best-fit percentage than N4SID model. The ARX achieved the best fit percentage of 71.76 % when the na and nb tuned at 6 and 4, respectively with the errors achieved 3.127 for MSE and 1.768 for RMSE. Then the N4SID achieved the best fit percentage of 71.75% at the seventh-order tuning with MSE and RMSE are 3.1586 and 1.7725, respectively. This algorithm will be used as a prediction model for MPC, in future.

关键词： Heuristic algorithms Noise Predictive models filtering algorithms Prediction algorithms Data models System identification Kalman filters Tuning Overfitting

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An Adaptive Low-Complexity Compression Algorithm for Vehicle Systems

An Adaptive Low-Complexity Compression Algorithm for Vehicle...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Chun-Hsiang Huang Shih-Lun Chen He-Sheng Chou Ya-Yun Huang Department of Electronic Engineering Chung Yuan Christian University Taoyuan Taiwan Department of Program on Semiconductor Manufacturing Technology Academy of Innovative Semiconductor and Sustainable Manufacturing National Cheng Kung University Tainan Taiwan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

Advanced Driver Assistance Systems (ADAS) are increasingly integrated into modern vehicles, relying on high-speed image transmission from on-board cameras to control systems. This study presents an adaptive lossless image compression algorithm designed to reduce transmission data while maintaining high image quality. By dynamically switching between Color Filter Array (CFA) compression and predictive compression, the proposed method achieves an average compression rate of 3.835 and a Peak Signal-to-Noise Ratio (PSNR) of 30.57 dB, significant improvements in efficiency, making it suitable for automotive applications.

关键词： Image quality Image coding PSNR Image color analysis Heuristic algorithms Automotive applications filtering algorithms Prediction algorithms Real-time systems Compression algorithms

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Research on Personalized Recommendation algorithms Based on Collaborative filtering

Research on Personalized Recommendation Algorithms Based on ...

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Computer Engineering and Intelligent Control (ICCEIC) International Conference on

作者： Hangrui Cao Zhan Shi Hao Yang School of Computer Engineering Nanjing Institute of technology Nanjing China

ISBN: (数字)9798331507992

ISBN: (纸本)9798331508005

Traditional recommendation algorithms often suffer from issues such as data sparsity, lack of memory association, and prediction accuracy. This paper proposes a collaborative filtering recommendation algorithm that integrates memory and balance factors. When analyzing user interests and preferences, there are optimal design from multiple aspects such as memory factors and novelty of objects. This paper introduces a memory weight function to reflect the temporal characteristics of user interests, and incorporates a balance factor into user similarity calculation to reduce the impact of popular items on user similarity calculation, thus improving the accuracy of user similarity calculation. Experimental validation is conducted on the MovieLens dataset using MAE as the evaluation metric. The results demonstrate significant improvements in the experimental metrics, indicating enhanced recommendation accuracy and improved resilience to data sparsity.

关键词： Measurement Accuracy Collaborative filtering Clustering algorithms filtering algorithms Prediction algorithms Indexes Recommender systems Resilience Intelligent control

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Inertial navigation attitude velocity and position algorithms using quaternion Scaled Unscented Kalman filtering

Inertial navigation attitude velocity and position algorithm...

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Annual Conference of Industrial Electronics Society

作者： Wassim Khoder Bienvenu Fassinut-Mombot Mohammed Benjelloun Laboratoire d'Analyse des Systèmes du Littoral Université du Littoral Côte d''Opale Calais France

In this paper, a scaled unscented Kalman filter (SUKF) based on the quaternion concept is designed for determination of the attitude, velocity and position parameters in inertial navigation system (INS) under large attitude error conditions. In this feedback filter, only bias effects are considered to be independent states and are used to compensate for navigation errors. To preserve the nonlinear nature of unit quaternion, the weighted mean computation for quaternions is derived in rotational space as a barycentric mean with renormalization and a multiplicative quaternion-error is used for predicted covariance computation of the quaternion because it represents the distance from the predicted mean quaternion. The updates are performed using quaternion multiplication which guarantees that quaternion normalization is maintained in the filter. Since the quaternion process noise increases the uncertainty in attitude orientation, modeling it as a vector part of quaternion is considered. Simulation and experimental results indicate a satisfactory performance of the newly developed model.

关键词： Inertial navigation filtering algorithms Quaternions Kalman filters Position measurement Accelerometers Algorithm design and analysis Uncertainty Velocity measurement Acceleration

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Research on algorithms of digital filtering under fuzzy conditions

Research on algorithms of digital filtering under fuzzy cond...

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International Conference on Actual Problems of Electronic Instrument Engineering (APEIE)

作者： Yu.M. Veshkurtsev E.D. Bychkov D.A. Titov Omsk State Technical University Russia

In this paper, a new digital filtering algorithm is developed for estimating nonstationary signals contaminated with additive random noise. The results of simulation of a fuzzy digital filter are presented.

关键词： filtering algorithms Digital filters

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P-Class Phasor Measurement Unit algorithms using adaptive filtering to enhance accuracy at off-nominal frequencies

P-Class Phasor Measurement Unit algorithms using adaptive fi...

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IEEE International Conference on Smart Measurements for Future Grids (SMFG)

作者： Andrew J. Roscoe Ibrahim F. Abdulhadi Graeme M. Burt Department of Electronic and Electrical Engineering University of Strathclyde Glasgow UK

While the present standard C.37.118-2005 for Phasor Measurement Units (PMUs) requires testing only at steady-state conditions, proposed new versions of the standard require much more stringent testing, involving frequency ramps and off-nominal frequency testing. This paper presents two new algorithms for “P Class” PMUs which enable performance at off-nominal frequencies to be retained at levels comparable to the performance for nominal frequency input. The performances of the algorithms are compared to the “Basic” Synchrophasor Estimation Model described in the new standard. The proposed algorithms show a much better performance than the “Basic” algorithm, particularly in the measurements of frequency and rate-of-change-of-frequency at off-nominal frequencies and in the presence of unbalance and harmonics.

关键词： Finite impulse response filter Frequency measurement filtering algorithms Phasor measurement units Correlation Frequency modulation IIR filters

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RLS Adaptive filtering Without A Desired Signal: QR algorithms And Architectures

RLS Adaptive Filtering Without A Desired Signal: QR Algorith...

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Asilomar Conference on Signals, Systems & Computers

作者： P.S. Lewis Mechanical and Electronic Engineering Los Alamos National Laboratory Los Alamos NM USA

Most adaptive filters require a desired signal for operation. However, in many applications the a priori knowledge consists of the signal-to-data cross-correlation vector rather than a desired signal. Recursive sample matrix inversion (SMI) algorithms exist for this "mixed' case. These SMI algorithms, which are based on the inversion of a data cor- relation matrix, have both numerical and structural short- comings. This paper demonstrates how to formulate the re- cursive solution to the mixed case as a least squares problem. This formulation leads to algorithms based on recursive QR decomposition implemented by either Givens or fast Givens rotations. Compared to the recursive SMI approach, these QR-based algorithms are more efficient, have better numerical properties, and exhibit greater structural regularity. Because of their structural regularity, the algorithms are easily implemented by either a triangular or linear systolic array.

关键词： Resonance light scattering Adaptive filters filtering algorithms Matrix decomposition Vectors Autocorrelation Least squares approximation Recursive estimation Kalman filters Laboratories

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Variable length adaptive filtering within incremental learning algorithms for distributed networks

Variable length adaptive filtering within incremental learni...

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Asilomar Conference on Signals, Systems & Computers

作者： Leilei Li Yonggang Zhang Jonathon A. Chambers Advanced Signal Processing Group Department of Electronic & Electrical Engineering Loughborough University Loughborough UK

ISBN: (纸本)9781424429400

In this paper we propose the use of variable length adaptive filtering within the context of incremental learning for distributed networks. algorithms for such incremental learning strategies must have low computational complexity and require minimal communication between nodes as compared to centralized networks. To match the dynamics of the data across the network we optimize the length of the adaptive filters used within each node by exploiting the statistics of the local signals to each node. In particular, we use a fractional tap-length solution to determine the length of the adaptive filter within each node, the coefficients of which are adapted with an incremental-learning learning algorithm. Simulation studies are presented to confirm the convergence properties of the scheme and these are verified by theoretical analysis of excess mean square error and mean square deviation.

关键词： Adaptive filters filtering algorithms Signal processing algorithms Analytical models Convergence Steady-state Adaptive signal processing Context Computational complexity Statistical distributions

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A Ray-driven Backprojector For Backprojection filtering And Filtered Backprojection algorithms

A Ray-driven Backprojector For Backprojection Filtering And ...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： G.L. Zeng G.T. Gullberg Department of Radiology University of Utah Salt Lake UT USA

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