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A Parallel Systematic resampling algorithm for High-Speed Particle Filters in Embedded Systems

CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2014年第11期33卷 3591-3602页

作者： Gan, Qifeng Langlois, J. M. Pierre Savaria, Yvon Polytech Montreal Dept Comp & Software Engn Montreal PQ Canada Polytech Montreal Dept Elect Engn Montreal PQ Canada

In this paper, we propose a parallel systematic resampling (PSR) algorithm for particle filters, which is a new form of systematic resampling (SR). The PSR algorithm makes iterations independent, thus allowing the resampling algorithm to perform loop iterations in parallel. A fixed-point version of the PSR algorithm is also proposed, with a modification to ensure that a correct number of particles is generated. Experiments show that the fixed-point implementation of the PSR algorithm can use as few as 22 bits for representing the weights, when processing 512 particles, while achieving results equivalent to a floating-point SR implementation. Four customized instructions were designed to accelerate the proposed PSR algorithm in Application-Specific Instruction-set Processors. These four custom instructions, when configured to support four weight inputs in parallel, lead to a 73.7 speedup over a floating-point SR implementation on a general-purpose processor at a cost of 47.3 K additional gates.

关键词： resampling algorithm Systematic resampling Particle Filters Fixed-point processing Application-specific instruction-set processors (ASIPs)

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Parallel Mesh Regularization and resampling algorithm for Improved Mesh Registration

Parallel Mesh Regularization and Resampling Algorithm for Im...

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4th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)

作者： Banerjee, Sumandeep Dutta, Somnath Biswas, Prabir Kumar Bhowmick, Partha Dept Elect & Elect Commun Engn Kanpur Uttar Pradesh India Indian Inst Technol Dept Comp Sci & Engn Kharagpur 721302 W Bengal India

ISBN: (纸本)9781479915880

In this paper, we present a fast and efficient algorithm for regularization and resampling of triangular meshes generated by 3D reconstruction methods such as stereoscopy, laser scanning etc. We also present a scheme for efficient parallel implementation of the proposed algorithm and the time gain with increasing number of processor cores.

关键词： resampling algorithm Medical Subject Headings Processor Core Mesh Mesh resampling efficient algorithm Parallel Lines stereoscopy Registration

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A Parallel resampling algorithm for Particle Filtering on Shared-Memory Architectures

A Parallel Resampling Algorithm for Particle Filtering on Sh...

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26th IEEE International Parallel and Distributed Processing Symposium (IPDPS) / Workshop on High Performance Data Intensive Computing

作者： Gong, Peng Basciftci, Yuksel Ozan Ozguner, Fusun Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA

ISBN: (纸本)9780769546766

Many real-world applications such as positioning, navigation, and target tracking for autonomous vehicles require the estimation of some time-varying states based on noisy measurements made on the system. Particle filters can be used when the system model and the measurement model are not Gaussian or linear. However, the computational complexity of particle filters prevents them from being widely adopted. Parallel implementation will make particle filters more feasible for real-time applications. Effective resampling algorithms like the systematic resampling algorithm are serial. In this paper, we propose the shared-memory systematic resampling (SMSR) algorithm that is easily parallelizable on existing architectures. We verify the performance of SMSR on graphics processing units. Experimental results show that the proposed SMSR algorithm can achieve a significant speedup over the serial particle filter.

关键词： particle filters resampling algorithm shared-memory architecture parallel computing

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A Reformulated Systematic resampling algorithm for Particle Filters and its Parallel Implementation in an Application-Specific Instruction-set Processor

A Reformulated Systematic Resampling Algorithm for Particle ...

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IEEE 56th International Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Gan, Qifeng Langlois, J. M. Pierre Savaria, Yvon Polytech Montreal Montreal PQ Canada

ISBN: (纸本)9781479900664

Particle filters (PFs) are computationally intensive, which prevents them from being widely used in some real-time applications with high throughput requirements. A parallel implementation is a feasible approach to enable using PFs in these applications. However, effective resampling algorithms such as the Systematic resampling (SR) algorithm are sequential in nature. In this paper, we propose a new form of the SR algorithm suitable for parallel implementation in an Application-Specific Instruction-set Processor (ASIP). Six custom instructions were designed for this reformulated SR algorithm. Experimental results show that the ASIP implementation of the reformulated SR algorithm, with four weights calculated in parallel, and eight categories defined by uniformly distributed numbers that are compared simultaneously to achieve a 30.6x speedup over the serial SR algorithm in a general-purpose processor. This comes at a cost of only 54K additional gates, or 68% overhead to be added to a base processor with 79K gates.

关键词： Particle filters PROCESSOR SAVANNAH RIVER OPERATIONS OFFICE Strontium Parallel Lines resampling algorithm ASIP gene real-time application program resampling

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A New resampling algorithm for Generic Particle Filters

A New Resampling Algorithm for Generic Particle Filters

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American Control Conference

作者： X. Fu Y. Jia J. Du F. Yu Seventh Research Division and the Department of Systems and Control Beihang University (BUAA) Beijing 100191 China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing 100876 China School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo 454000 Henan China

ISBN: (纸本)9781424474264

This paper is devoted to the resampling problem of particle filters. We firstly demonstrate the performance of classical resampling algorithm (also called as systematic resampling algorithm) using a novel metaphor, through which the existing defects of resampling algorithm is vividly reflected simultaneously. In order to avoid these defects, the exquisite resampling (ER) algorithm is induced which involves some exquisite actions such as comparing the weights by stages and generating the new particles based on quasi-Monte Carlo method. Simulations indicate that the proposed ER algorithm can reduce the sample impoverishment effectively and improve the accuracy of estimation evidently, which confirm that ER algorithm is a competitive alternative to resampling algorithm.

关键词： resampling algorithm Particle filters

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State of charge estimation method for lithium-ion batteries based on adaptive central difference particle filter with weight reconstruction

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JOURNAL OF ENERGY STORAGE 2025年 106卷

作者： Yun, Xiang Zhang, Xin Wang, Chao Fan, Xingming Guilin Univ Elect Technol Coll Elect Engn & Automat Guilin 541004 Peoples R China

The particle filter (PF) has been widely used for state of charge (SOC) estimation. However, the particle degradation phenomenon will affect the estimation accuracy. In response to this issue, a state of charge estimation method for lithium-ion batteries based on adaptive central difference particle filter with weight reconstruction (ACDPF-WR) is designed in this paper. This method combines the preferred importance density function and the optimized resampling strategy. Firstly, the central difference Kalman filter (CDKF) is used to update the sampled particles to reduce the influence of particle degradation. Secondly, the Gaussian process regression (GPR) model of particles and weights is constructed by combining the offline learning experimental battery data set, and the GPR is used to generate the weight distribution of the particle filter. Then, an adaptive step size mechanism is introduced, which determines the optimal step size by calculating the mean square error of the weight distribution under different steps. Finally, the weight distribution generated by the central difference particle filter (CDPF) based on the optimal step size is combined with the typical resampling algorithm to select high-quality particles to achieve the optimal estimation. The adaptability and robustness of the algorithm are verified under Beijing Dynamic Stress Test (BJDST), US06 Highway Driving Schedule (US06), and Dynamic Stress Test (DST) conditions, and evaluated by mean absolute error (MAE) and root mean square error (RMSE) indicators. The average comparison results of the three working conditions show that the MAE of ACDPF-WR is 54.1 % higher than that of EPF and 24.3 % higher than that of CDPF, and the RMSE of ACDPF-WR is 64.6 % higher than that of EPF and 24.5 % higher than that of CDPF. The proposed algorithm achieves better performance and provides new insights and methods for the optimization and improvement of the battery management system.

关键词： State of charge Lithium-ion battery Central difference particle filter Guassian process regression resampling algorithm

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Improving GALDIT-based groundwater vulnerability predictive mapping using coupled resampling algorithms and machine learning models

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JOURNAL OF HYDROLOGY 2021年 598卷 126370-126370页

作者： Barzegar, Rahim Razzagh, Siamak Quilty, John Adamowski, Jan Pour, Homa Kheyrollah Booij, Martijn J. McGill Univ Dept Bioresource Engn 21111 Lakeshore Ste Anne De Bellevue PQ H9X 3V9 Canada Wilfrid Laurier Univ Dept Geog & Environm Studies Waterloo ON Canada Univ Tabriz Fac Nat Sci Dept Earth Sci Tabriz Iran Univ Waterloo Dept Civil & Environm Engn Waterloo ON Canada Univ Twente Fac Engn Technol Dept Water Engn & Management Enschede Netherlands

Developing accurate groundwater vulnerability maps is important for the sustainable management of groundwater resources. In this research, resampling methods [e.g., Bootstrap Aggregating (BA) and Disjoint Aggregating (DA)] are combined with machine learning (ML) models, namely eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), and Random Forest (RF), to improve the GALDIT groundwater vulnerability mapping framework that considers Groundwater occurrence (G) (i.e., aquifer type), Aquifer hydraulic conductivity (A), depth to groundwater Level (L), Distance from the seashore (D), Impact of existing seawater intrusion status (I), and aquifer Thickness (T). The proposed approach overcomes the subjectivity of the weights and ratings given to the six variables in the GALDIT framework (via the ML methods) and helps address the small dataset issue (via resampling methods) common to groundwater vulnerability predictive mapping. Considering the Shabestar Plain aquifer, situated in the northeast of Lake Urmia (Iran), the predicted vulnerability indices from GALDIT were adjusted using total dissolved solid (TDS, an indicator of drinking water quality) concentrations, and were modeled by the ML models. Pearson's correlation coefficient (r) and distance correlation (DC) between the predicted vulnerability indices and TDS were used to validate the models. Using a validation set, the GALDIT framework (r = 0.447 and DC = 0.511) was compared against the best performing standalone (XGBoost-GALDIT, r = 0.613, DC = 0.647) and coupled resampling (BA-XGBoost-GALDIT, r = 0.659, DC = 0.699 and DA-RF-GALDIT, r = 0.616, DC = 0.662) ML models, revealing that the proposed framework significantly increases r and DC metrics. In general, the BA resampling method led to better performing ML models than DA. However, in all cases, it was found that integrating resampling methods and ML models are promising tools to

关键词： resampling algorithm Groundwater vulnerability Coastal aquifer Machine learning Hybrid model

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Precise Amplitude and Phase Determination Using resampling algorithms for Calibrating Sampled Value Instruments

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SENSORS 2020年第24期20卷 7345-7345页

作者： Chen, Yeying Mohns, Enrico Seckelmann, Michael de Rose, Soeren Phys Tech Bundesanstalt PTB Instrument Transformers & Sensors Elect Energy Measuring Tech D-38116 Braunschweig Germany

Sampling-based calibration systems for calibrating "Sampled Value" (SV)-based instruments for substation automation require synchronised and time-aligned sampling processes. As the signal frequency of the power grid is always asynchronous to the standardised sampling frequencies according to IEC 61869-9, the sampled waveforms of the calibration system and of the SV-based device under test can be resampled to be synchronised and to allow better accuracy in the following measurements based on the Discrete Fourier Transform (DFT) of the resampled waveforms. The paper presents simulations and results for different resampling algorithms. A modified sinc interpolation method with a finite impulse response (FIR) is presented. The deviation of the results for the root mean square (RMS) and phase angle is in the order of 10(-8) V/V (or rad) for normalised frequencies of up to 20% of the sampling frequency. No practical degradation in the presence of noise and harmonics could be observed. In addition, laboratory experiments demonstrate the realization of the proposed resampling process in the future SV-based calibration systems for SV-based instrumentation.

关键词： resampling algorithm sampled values SV-based instrumentation SV-based calibration phase synchronisation sinc interpolation

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Improved resampling algorithms Based on Deterministic and Partial resampling

Improved Resampling Algorithms Based on Deterministic and Pa...

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2016 International Conference on Mathematical, Computational and Statistical Sciences and Engineering（MCSSE 2016）

作者： Chao HE Wei-sheng XU Peng CHEN Department of Electrical & Information CDHKTongji University School of Electronics and Information Engineering Tongji University School of Mechanical Engineering Southwest Jiaotong University

Particle Filter is widely used to track objects in autonomous driving. resampling is one of the most important steps in the particle filter. In implementations, resampling is regarded as a bottleneck because of the increased complexity. To handle such kind of difficulties, an improved resampling algorithm is proposed. Firstly, the most frequently applied resampling algorithms for particle filters are introduced in this paper. A theoretical analysis is provided and the differences among these resampling algorithms are revealed. This facilitates a comparison of the algorithms about their resampling quality and computational complexity. Through the simulations, the results in the theoretical analysis are verified. It is found that optimized partial deterministic resampling is suitable for tracking in terms of a large amount of objects.

关键词： Particle filter resampling algorithm Tracking

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ROBUST REGRESSION WITH A DISTRIBUTED INTERCEPT USING LEAST MEDIAN OF SQUARES

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 1994年第1期17卷 65-75页

作者： ROUSSEEUW, PJ WAGNER, J UNIV INSTELLING ANTWERP B-2610 WILRIJKBELGIUM UNIV HANNOVER W-3000 HANNOVERGERMANY

Ordinary least squares (OLS) regression has a breakdown point of 0%, whereas the least median of squares (LMS) method and the corresponding reweighted least squares (RLS) method can withstand a substantial number of outliers in the data. In this paper the LMS/RLS method is extended to robust estimation of a distributed intercept model, and an algorithm for this situation is proposed. This method is applied to estimation of earnings functions with different intercepts for industries, based on data from five countries. Contrary to a widely shared view, the patterns of interindustry wage differentials seem to be different across the countries.

关键词： DUMMY VARIABLES EARNINGS FUNCTION resampling algorithm

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