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检索条件"主题词=filtering algorithms"

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Decentralized Multi-Agent Active Search and Tracking when Targets Outnumber Agents

Decentralized Multi-Agent Active Search and Tracking when Ta...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Arundhati Banerjee Jeff Schneider School of Computer Science Carnegie Mellon University Pittsburgh USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Multi-agent multi-target tracking has a wide range of applications, including wildlife patrolling, security surveillance or environment monitoring. Such algorithms often make restrictive assumptions: the number of targets and/or their initial locations may be assumed known, or agents may be pre-assigned to monitor disjoint partitions of the environment, reducing the burden of exploration. This also limits applicability when there are fewer agents than targets, since agents are unable to continuously follow the targets in their fields of view. Multi-agent tracking algorithms additionally assume inter-agent synchronization of observations, or the presence of a central controller to coordinate joint actions. Instead, we focus on the setting of decentralized multi-agent, multi-target, simultaneous active search-and-tracking with asynchronous interagent communication. Our proposed algorithm DecSTER uses a sequential monte carlo implementation of the Probability Hypothesis Density filter for posterior inference combined with Thompson sampling for decentralized multi-agent decision making. We compare different action selection policies, focusing on scenarios where targets outnumber agents. In simulation, we demonstrate that DecSTER is robust to unreliable inter-agent communication and outperforms information-greedy baselines in terms of the Optimal Sub-Pattern Assignment (OSPA) metric for different numbers of targets and varying teamsizes.

关键词： Target tracking Uncertainty Surveillance Robot kinematics Wildlife Object detection filtering algorithms

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Probabilistic 3D Multi-Object Cooperative Tracking for Autonomous Driving via Differentiable Multi-Sensor Kalman Filter

Probabilistic 3D Multi-Object Cooperative Tracking for Auton...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Hsu-Kuang Chiu Chien-Yi Wang Min-Hung Chen Stephen F. Smith Robotics Institute of Carnegie Mellon University NVIDIA Research

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Current state-of-the-art autonomous driving vehicles mainly rely on each individual sensor system to perform perception tasks. Such a framework’s reliability could be limited by occlusion or sensor failure. To address this issue, more recent research proposes using vehicle-to-vehicle (V2V) communication to share perception information with others. However, most relevant works focus only on cooperative detection and leave cooperative tracking an underexplored research field. A few recent datasets, such as V2V4Real, provide 3D multi-object cooperative tracking benchmarks. However, their proposed methods mainly use cooperative detection results as input to a standard single-sensor Kalman Filter-based tracking algorithm. In their approach, the measurement uncertainty of different sensors from different connected autonomous vehicles (CAVs) may not be properly estimated to utilize the theoretical optimality property of Kalman Filter-based tracking algorithms. In this paper, we propose a novel 3D multi-object cooperative tracking algorithm for autonomous driving via a differentiable multi-sensor Kalman Filter. Our algorithm learns to estimate measurement uncertainty for each detection that can better utilize the theoretical property of Kalman Filter-based tracking methods. The experiment results show that our algorithm improves the tracking accuracy by 17% with only 0.037x communication costs compared with the state-of-the-art method in V2V4Real. Our code and videos are available at the URL and the URL.

关键词： Uniform resource locators Training Three-dimensional displays Measurement uncertainty filtering algorithms Probabilistic logic Feature extraction

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FunM2C: A Filter for Uncertainty Visualization of Multivariate Data on Multi-Core Devices

FunM2C: A Filter for Uncertainty Visualization of Multivaria...

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Uncertainty Visualization: Applications, Techniques, Software, and Decision Frameworks, IEEE Workshop on

作者： Gautam Hari Nrushad Joshi Zhe Wang Qian Gong Dave Pugmire Kenneth Moreland Chris R. Johnson Scott Klasky Norbert Podhorszki Tushar M. Athawale Indiana University Oak Ridge National Laboratory SCI Institue University of Utah

ISBN: (数字)9798331527600

ISBN: (纸本)9798331527617

Uncertainty visualization is an emerging research topic in data visualization because neglecting uncertainty in visualization can lead to inaccurate assessments. In this paper, we study the propagation of multivariate data uncertainty in visualization. Although there have been a few advancements in probabilistic uncertainty visualization of multivariate data, three critical challenges remain to be addressed. First, the state-of-the-art probabilistic uncertainty visualization framework is limited to bivariate data (two variables). Second, existing uncertainty visualization algorithms use computationally intensive techniques and lack support for cross-platform portability. Third, as a consequence of the computational expense, integration into production visualization tools is impractical. In this work, we address all three issues and make a threefold contribution. First, we take a step to generalize the state-of-the-art probabilistic framework for bivariate data to multivariate data with an arbitrary number of variables. Second, through utilization of VTK-m’s shared-memory parallelism and cross-platform compatibility features, we demonstrate acceleration of multivariate uncertainty visualization on different many-core architectures, including OpenMP and AMD GPUs. Third, we demonstrate the integration of our algorithms with the ParaView software. We demonstrate the utility of our algorithms through experiments on multivariate simulation data with three and four variables.

关键词： Uncertainty Conferences Software algorithms Data visualization Production Computer architecture Parallel processing filtering algorithms Probabilistic logic Software

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Detection of Water Quality in Sediment Filter for Water Purification System

Detection of Water Quality in Sediment Filter for Water Puri...

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E-mobility, Power Control and Smart Systems (ICEMPS), International Conference on

作者： T. Kalavathidevi R. Mouleeshuwarapprabhu E.B. Priyanka V.K. Shubhashree K. Srihariramana T.G. Thilochan Electronics and Instrumentation Engineering Kongu Engineering College Erode India (SrG) Electronics and Instrumentation Engineering Kongu Engineering College Erode India Department of Mechatronics Kongu Engineering College Erode India Electronics and Instrumentation Engineering Kongu Engineering College Erode India

ISBN: (数字)9798350394399

ISBN: (纸本)9798350394405

Efficient water purification is vital for ensuring access to clean and safe drinking water. One essential component of water purification systems is the sediment filter, responsible for removing particulate matter and debris from the water. Monitoring and maintaining the performance of sediment filters are critical to guarantee the overall effectiveness of the water purification process. This study focuses on the development of a detection method to assess the water quality within sediment filters. The proposed detection method combines advanced sensing technologies and data analysis techniques to continuously evaluate the sediment filter's efficiency and identify potential issues. The system can promptly detect deviations from optimal performance by integrating real-time monitoring of key water quality parameters, such as turbidity, sediment concentration, and pressure differentials. Additionally, machine learning algorithms are employed to analyze the collected data, enabling the system to predict sediment filter clogging and degradation trends.

关键词： Temperature sensors Temperature measurement Purification Image color analysis Turbidity filtering algorithms Water pollution

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Clock Bias Prediction by Application of Variational Bayesian Adaptive Kalman Filter

Clock Bias Prediction by Application of Variational Bayesian...

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European Frequency and Time Forum (EFTF)

作者： Dehao Chen Hang Yi Dandan Li Ang Li Huijun Yang Xueyun Wang Science and Technology on Metrology and Calibration Laboratory Beijing Institute of Radio Metrology and Measurement Beijing China

ISBN: (数字)9798350354270

ISBN: (纸本)9798350354287

To improve the performance of traditional Kalman filtering algorithms in clock bias prediction., this paper proposes the application of variational Bayesian adaptive Kalman filtering algorithm to achieve clock bias prediction. This method addresses the shortcomings of traditional Kalman filters that require accurate observation noise models and system noise models in effective application. It proposes a combined algorithm of Sage Husa adaptive Kalman filtering algorithm and variational Bayesian Kalman algorithm using inverse Wishart distribution to iteratively estimate and correct observation noise $R$ and system noise Q., estimate the covariance of system state and observation noise., and apply it to Kalman filters to improve the adaptive ability of Kalman filter clock bias prediction. Simulation experiments on filtering and clock bias prediction based on precise post ephemeris satellite clock bias data. The results show that compared with traditional Kalman filters., applying variational Bayesian adaptive Kalman filters to clock bias prediction has improved the prediction accuracy to a certain extent.

关键词： Adaptation models Satellites Adaptive systems filtering Noise filtering algorithms Prediction algorithms Bayes methods Kalman filters Clocks

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Distributed Diffusion-Based Manifold Particle Filters for Orientation Estimation

Distributed Diffusion-Based Manifold Particle Filters for Or...

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European Signal Processing Conference (EUSIPCO)

作者： Claudio J. Bordin Marcelo G.S. Bruno Universidade Federal do ABC Santo André SP Brazil Instituto Tecnológico de Aeronáutica São José dos Campos SP Brazil

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Determining orientation is a fundamental problem in fields such as robotics, computer vision, and autonomous driving. In three-dimensional space, orientations can be represented in many forms, including unit quaternions or points of the Special Orthogonal Group (SO(3)). These representations present challenges due to their non-Euclidean nature. This paper addresses the problem of computing a joint Bayesian estimate of a rotation given the observations of a set of receivers that consist of corrupted and transformed versions of the true orientation. We evaluate the performance of the described algorithms via numerical simulations in terms of accuracy and communication requirements.

关键词： Manifolds Quaternions Signal processing algorithms Europe Estimation Receivers filtering algorithms Numerical simulation Particle filters Numerical models

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Estimation of Vehicle Sideslip Angle and Tire Cornering Stiffness Considering Mass Mismatch

Estimation of Vehicle Sideslip Angle and Tire Cornering Stif...

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Conference on Vehicle Control and Intelligence (CVCI)

作者： Zhongtao Wu Lilin Shen Shuo Bai Fengwei Dong Guodong Yin School of Mechanical Engineering Southeast University Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China

ISBN: (数字)9798331504892

ISBN: (纸本)9798331504908

As vehicles are utilized for transportation, their actual load is changeable, and the total mass is not remaining constant. Mismatch in mass parameters can lead to inaccuracies in the vehicle model, thereby diminishing the estimation accuracy of crucial state parameters like tire cornering stiffness (TCS) and sideslip angle (SA). To tackle these issues, this research proposes a co-estimation algorithm for the front and rear TCS and SA of vehicles based on Strong Tracking Unscented Kalman Filter (STUKF). Integrating the strong tracking concept into the Unscented Kalman Filter (UKF) by introducing a fading factor matrix, and real time adjustment of state error covariance matrix based on residual sequence orthogonality criterion to achieve swift correction of state estimation vector. To assess the efficacy of the STUKF algorithm under different road conditions, this research select different road friction-coefficients and compare the results with those obtained from EKF and RLS algorithms. The findings illustrate that the STUKF algorithm has enhanced the accuracy of estimation and demonstrates strong anti-interference capabilities for different road conditions and strong robustness to state mutations.

关键词： Accuracy Roads Transportation filtering algorithms Tires Robustness Vectors Kalman filters Covariance matrices State estimation

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A High Accuracy Extraction Method of Point Cloud Vectors for Guiding Robotic Arm Movement

A High Accuracy Extraction Method of Point Cloud Vectors for...

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System Simulation Technology and its Application (CCSSTA), IEEE China Conference on

作者： Ruoyu He Yihang Li Min Jia Peng Zhang Bin Liu School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China

ISBN: (数字)9798350366600

ISBN: (纸本)9798350366617

A path planning method based on point cloud for robotic arm movement was proposed, which provides the 3D point and its vector direction for each moving position. First, an effective filtering algorithm is utilized to smooth the original point cloud. Then, the least squares method is employed to perform plane fitting on local regions of the filtered point cloud. Finally, the points with the corresponding normal vectors could be obtained for guiding the robotic arm movement. Experiments were designed to evaluate our method. Two filtering algorithms are carefully compared and chosen to simultaneously guarantee the accuracy of the gathered points and normal vectors, an evaluation metric is proposed. The point cloud of a standard sphere was acquired and fitted to calculate the sphere center. Each point and its normal vector determine one spatial line. The distance from the sphere center to each spatial line is defined as the quantitative indicator of the evaluation metric. Experimental results proved that the proposed method and evaluation metric are feasible and effective for precise motion control of robotic arms.

关键词： Point cloud compression Measurement Accuracy Three-dimensional displays filtering algorithms Manipulators Vectors Path planning Systems simulation Standards

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Interactive Counterfactual Exploration of Algorithmic Harms in Recommender Systems

Interactive Counterfactual Exploration of Algorithmic Harms ...

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IEEE Visualization in Data Science (VDS)

作者： Yongsu Ahn Quinn K Wolter Jonilyn Dick Janet Dick Yu-Ru Lin University of Pittsburgh Quest Diagnostics

ISBN: (数字)9798331528423

ISBN: (纸本)9798331528430

Recommender systems have become integral to digital experiences, shaping user interactions and preferences across various platforms. Despite their widespread use, these systems often suffer from algorithmic biases that can lead to unfair and unsatisfactory user experiences. This study introduces an interactive tool designed to help users comprehend and explore the impacts of algorithmic harms in recommender systems. By leveraging visualizations, counterfactual explanations, and interactive modules, the tool allows users to investigate how biases such as miscalibration, stereotypes, and filter bubbles affect their recommendations. Informed by in-depth user interviews, this tool benefits both general users and researchers by increasing transparency and offering personalized impact assessments, ultimately fostering a better understanding of algorithmic biases and contributing to more equitable recommendation outcomes. This work provides valuable insights for future research and practical applications in mitigating bias and enhancing fairness in machine learning algorithms.

关键词： Machine learning algorithms Data visualization filtering algorithms Data science Interviews Recommender systems

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Design of Fifth-Order Chebyshev Bandpass Filter Using Group Delay of the Reflection Coefficient Based on Space Mapping

Design of Fifth-Order Chebyshev Bandpass Filter Using Group ...

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IEEE International Conference on Computational Electromagnetics (ICCEM)

作者： Yihang Li Feng Feng Jingpei Zhang Wei Liu Kaixue Ma Qi-Jun Zhang School of Microelectronics Tianjin University Tianjin China Department of Electronics Carleton University Ottawa ON Canada

ISBN: (数字)9798350383317

ISBN: (纸本)9798350383324

The group delay characteristics of the reflection coefficient for circuit design are introduced. The waveguide filter is simulated with the group delay of the reflection coefficient of the circuit as the target. Due to the symmetry of the filter, the simulation parameters are halved. After obtaining the initial optimization curve, the space mapping algorithm is applied to filter design optimization. Finally, a 5-pole Chebyshev bandpass filter has been successfully designed according to the proposed design method.

关键词： Band-pass filters Design methodology Space mapping Electromagnetic waveguides filtering algorithms Chebyshev approximation filtering theory

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