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检索条件"主题词=filtering algorithms"

共 10367 条记录，以下是1001-1010 订阅

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Autonomous Navigation Algorithm for GEO Satellites During Hall Thruster Trajectory Control in All-Time

Autonomous Navigation Algorithm for GEO Satellites During Ha...

引用

Chinese Automation Congress (CAC)

作者： Xiao Liu Sihong Gao Jupin Liang Kun Xiang Zhengyong Guo Shanghai Key Laboratory of Aerospace Intelligent Control Technology Shanghai Aerospace Control Technology Institute Shanghai CHINA

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

This paper mainly studies the autonomous navigation and orbit propagation algorithm of the Hall electric propulsion as the thrust control device for geostationary orbit satellites during the thrust control period. Compared with low-altitude satellites, high-altitude satellites generally have relatively poor GNSS velocity measurement accuracy during thrust control, as well as small thrust generated by the Hall electric propulsion, and the tachometer on the large-inertia, large-mass satellite cannot accurately measure the thrust control acceleration, resulting in a decline in the original autonomous navigation accuracy during thrust control. This paper proposes a joint autonomous navigation algorithm for the Hall electric propulsion during thrust control using the EKF and local optimal filtering. The EKF filter suppresses the measurement white noise of the GNSS positioning data, and then the local optimal filter estimates the Hall electric propulsion acceleration and feeds it back to the EKF filter algorithm in real time to improve the navigation accuracy of the Hall electric propulsion during operation. Simulation results show that the joint autonomous navigation algorithm based on the above filtering method can accurately estimate the size of the Hall electric propulsion acceleration, and can effectively improve the autonomous navigation accuracy during continuous small thrust control.

关键词： Global navigation satellite system Satellites Accuracy filtering Electric variables measurement Propulsion filtering algorithms Extraterrestrial measurements Velocity measurement Autonomous robots

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A Compensation Method for Group Target State and Structure Estimation based on GLMB

A Compensation Method for Group Target State and Structure E...

引用

Chinese Control and Decision Conference, CCDC

作者： Jianshu Liu Weifeng Liu Boyang Ma Shaanxi University of Science and Technology Xian China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

To overcome the coupling problem of structure and state estimation in cluster target tracking, this paper introduces a compensation algorithm for group target estimation founded on Generalized Labeled Multi-Bernoull filter. Firstly, based on the dynamic model of the cluster target, the collaborative noise model of the cluster target is described and established. Secondly, the structure of the cluster is estimated to obtain the cooperative noise compensation term and compensate the group target prediction state under the cooperative condition. Finally, under the framework of GLMB filter, the update status and estimated structure of the cluster are obtained, and the prediction structure of the cluster is further compensated.

关键词： Couplings Target tracking Heuristic algorithms Noise Clustering algorithms filtering algorithms Prediction algorithms

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Geometric Algebra Based Kalman Filter Trajectory Prediction

Geometric Algebra Based Kalman Filter Trajectory Prediction

引用

Aerospace Engineering and Systems (ISAES), International Symposium on

作者： Taiyu Li Weidong Jiao Tianjin Key Laboratory of Intelligent Signal and Image Processing Civil Aviation University of China Tianjin China

ISBN: (数字)9798350350418

ISBN: (纸本)9798350350425

To improve the safety and efficiency of aviation operation, a Kalman filtered trajectory prediction algorithm based on geometric algebra is proposed in this space. Firstly, the Kalman filter equations for trajectory prediction in geometric algebraic space are given by taking position, velocity and acceleration as state variables and position, velocity and acceleration in a typical trajectory as observations. Secondly, the matrix operation in the geometric algebraic space is simplified to the matrix operation by establishing a composite geometric algebraic space. Then, the versor operator in the conformal geometric algebraic space is introduced into the Kalman filter, and the steps of geometric algebra-based Kalman filter trajectory prediction are given to improve the prediction rate and accuracy. The experimental results show that the proposed trajectory prediction algorithm has the highest accuracy and the operation efficiency is improved by 27.66 % compared with the Kalman filter algorithm in Euclidean space and the DBSCAN clustering algorithm.

关键词： Accuracy Algebra Clustering algorithms filtering algorithms Predictive models Prediction algorithms Mathematical models Trajectory Safety Kalman filters

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Adjacent Multi-extended Targets Tracking in Traffic Scenes

Adjacent Multi-extended Targets Tracking in Traffic Scenes

引用

Progress in Electromagnetic Research Symposium (PIERS)

作者： Yunlian Tian Wunjun Li Wei Yi School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

This paper intends to address the problem of tracking adjacent multi-extended targets in traffic scenes. Traditional extended target tracking algorithms have been widely developed based on the clustering operation. However, clustering errors always occur for adjacent multi-extended targets, posing a high risk of track cross and coalescence as these extended targets share a common focus point to update their state. In this paper, we propose a separation probability model for the case of an arbitrary number of adjacent extended objects. Furthermore, we incorporate the model into the ordinary joint probabilistic data association (JPDA) filter to circumvent the drawback of clustering. The effectiveness of the proposed algorithm is validated through numerical simulations.

关键词： Target tracking Clustering algorithms filtering algorithms Probabilistic logic Numerical simulation Density functional theory Data models

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On Analyzing Filters with Bayesian Parameter Inference and Poisson-Sampled Observations

On Analyzing Filters with Bayesian Parameter Inference and P...

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IEEE Conference on Decision and Control

作者： Aneel Tanwani CNRS - LAAS University of Toulouse CNRS Toulouse France

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

The problem of state estimation in continuoustime linear stochastic systems is considered with several constraints on the available information. It is stipulated that the model of the system contains several unknown parameters and the observation process is randomly time-sampled. The classical solution due to Kalman-Bucy cannot be implemented in that case, and we revisit the idea of partitioning the set of unknown parameters, and consider a multiple filters corresponding to each possible value of the unknown parameter. The posterior distribution of the unknown parameters conditioned upon available observations is computed from Bayes’ rule. The resulting state estimate is a weighted sum of the state estimates generated by multiple Kalman filters, where the weights are determined by the posterior distribution of the unknown parameters. We analyze the performance of the algorithm by looking at its asymptotic behavior and establishing boundedness of the error covariance matrix.

关键词： Stochastic systems filtering algorithms Inference algorithms Bayes methods Kalman filters State estimation Covariance matrices

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Nonlinear Estimation Using Optimized Extension Based Multiple Conversion Approach with Generalized Conversion for Target Tracking

Nonlinear Estimation Using Optimized Extension Based Multipl...

引用

Chinese Automation Congress (CAC)

作者： Ruiqing Xi Jian Lan Le Zhang School of Electronic and Information Engineering Xi'an Jiaotong University Xi'an P. R. China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

For estimation, minimum mean square error is one of the most widely accepted criteria. The optimal MMSE estimate is the posterior mean. For linear Gaussian systems, the posterior mean can be calculated easily. For nonlinear problems, it is hard to be obtained, although the posterior distribution can be solved by the joint distribution of the estimand and its measurement. Aim at this, the multiple conversion approach (MCA) uses several predesigned hypothesized distributions to match the true joint distribution. To match the truth better, the recently proposed optimized conversion extension (OCE) approach introduces one more distribution into the MCA. It has also proven that the MCA using the OCE outperforms the original one. However, practical nonlinear estimation problems are diverse. For a nonlinear problem, obtaining such an estimator which outperforms the existing estimators is hard. In this paper, considering the effectiveness of the recently proposed generalized conversion based filter (GCF) for conversions with high dimensions, we introduce it within the OCE framework. It is proven that the obtained new estimator called OCE-G can outperform the MCA using the original OCE and match the truth more sufficiently. Finally, a filtering algorithm using the OCE-G based on the interacting multiple conversion is proposed for dynamic problems. Compared with several popular nonlinear filters, a bearing-only filtering problem is used and simulated to demonstrate the effectiveness of the proposed approach and algorithm.

关键词： Matched filters Target tracking Automation filtering Heuristic algorithms Simulation Estimation Nonlinear filters Mean square error methods filtering algorithms

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Analyzing Adaptive Filters for the Separation of Fetal Electrocardiogram in Non-Invasive Monitoring: A Comparative Approach

Analyzing Adaptive Filters for the Separation of Fetal Elect...

引用

India Council International Subsections Conference (INDISCON), IEEE

作者： S Sunish J Aravinth Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798350376753

ISBN: (纸本)9798350376760

This paper focuses on the development and comparison of adaptive filters for the critical task of distinguishing fetal electrocardiogram (ECG) from abdominal ECG. Addressing the challenges posed by invasive and non-invasive acquisition methods, four adaptive filters—Leat Mean Square (LMS),Normalised Least Mean Square (NLMS),Recursive Least Square (RLS), and a lattice-based algorithm—are applied to abdominal ECG mixed with noise. While all algorithms successfully separate fetal ECG, none achieve a perfect signal, as residual noise persists. The Recursive Least Squares (RLS) algorithm stands out, preserving the most features from the reference waveform, albeit with a trade-off of reduced amplitude. Comprehensive evaluation through Mean Squared Error (MSE) and Signal-to-Noise Ratio (SNR) values demonstrates the superior performance of the RLS algorithm in minimizing noise and enhancing signal reproducibility. The RLS algorithm has a SNR value of 15.45 dB, PSNR value of 15.11 dB and MSE value of 0.002.

关键词： Adaptive filters Electrocardiography filtering algorithms Reproducibility of results Monitoring Signal to noise ratio

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Distributed State Estimation with Random Switching Topology Under Sensor Saturation Constraints

Distributed State Estimation with Random Switching Topology ...

引用

Chinese Automation Congress (CAC)

作者： Fengzeng Zhu Zhongqi Li Ancai Zhang Guochen Pang Xiao Liang School of Automation and Electrical Engineering Linyi University Linyi China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

This paper presents a distributed robust filtering technique for sensor networks that incorporates randomly switching communication topologies and sensor saturation nonlinearities. In the sensor network, numerous sensor nodes are placed to detect and measure the target plant, transmitting data to a remote filter via the network. Among the filter network, each local estimator receives measurement data from sensor nodes and estimation data from neighboring nodes through a randomly varying topology. The sensor network employs a Bernoulli binary distribution to model random saturation nonlinearity, while random switching topologies are characterized using a non-homogeneous Markov chain. Next, a sufficient condition for the existence of a distributed filter is presented using the linear matrix inequality (LMI). Lastly, a numerical example illustrates the viability of the designed strategy.

关键词： Wireless sensor networks Sufficient conditions Network topology filtering Switches filtering algorithms Robustness Topology Numerical models State estimation

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Hierarchical Estimation of Vehicle Key Parameters Using KF-RLS Method

Hierarchical Estimation of Vehicle Key Parameters Using KF-R...

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

作者： Ao Chi Juan Wei Tongming Wei Guanyu Song Liangchun Qin Min Wei Yong Gao Liping Lu Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhang Hubei China Technical Center SAIC GM Automobile Co Ltd Liuzhou Guangxi China School of Automotive Engineering Wuhan University of Technology Wuhang Hubei China School of Computer and Artificial Intelligence Wuhan University of Technology Wuhang Hubei China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

In order to achieve accurate estimation of critical vehicle and road parameters that cannot be directly measured, without the addition of extra sensors, this paper proposes a joint parameter estimation algorithm combining Kalman filtering and Recursive Least Squares (KF-RLS). In this study, Kalman filtering (KF) is employed to reduce the state noise detected by sensors, while Recursive Least Squares (RLS) utilizes the filtered state data in conjunction with the longitudinal dynamics model of the vehicle for parameter estimation. After the design of the algorithm, a hierarchical estimation strategy was adopted; this strategy first estimates the mass of vehicle, subsequently determines the position of the center of gravity, and finally identifies the tire-road friction coefficient based on these estimates. Simulation experiments conducted in the TruckSim environment demonstrate that the proposed KF-RLS joint estimation algorithm exhibits superior performance in terms of accuracy and anti-interference capability compared to the single RLS method.

关键词： Parameter estimation Accuracy Heuristic algorithms Friction Noise Estimation filtering algorithms Sensors Kalman filters Vehicle dynamics

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Satellite Radar Group Target Tracking Algorithm Using TP-GLMB Filter

Satellite Radar Group Target Tracking Algorithm Using TP-GLM...

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International Conference on Electronic Engineering and Informatics (EEI)

作者： Xuan Chen Zhiyong Lei Yu Li Communication and Information Engineering Nanjing research institution of electronic technology Nanjing China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

In the group target detection scenario of low orbit satellite radar, the coexistence of distinguishable targets and indistinguishable targets in the group and the dynamic transformation lead to the degradation of tracking performance. To solve the problem, a GLMB filtering algorithm based on target type discrimination for partially distinguishable group targets was proposed. In the process of prediction and update, the target type parameter and DBSCAN clustering algorithm are introduced, and the centroid equivalent measurement is used to replace the indistinguishable target measurements. Simulation results show that the proposed algorithm has better tracking performance when distinguishable and indistinguishable targets coexist and change dynamically compared with existing algorithms.

关键词： Target tracking Satellites Heuristic algorithms Spaceborne radar Simulation Clustering algorithms Radar detection filtering algorithms Radar tracking Prediction algorithms

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