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An Improved High-Degree Cubature Particle Filter and its Application in Bearing-only Tracking 认领 引用 被引量：1
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作者 Yanqi Niu Dandan Zhu Yaan Li 《哈尔滨工程大学学报（英文版）》 CSCD 2026年第1期300-311,共12页
In this study,a fifth-degree cubature particle filter(5CPF)is proposed to address the limited estimation accuracy in traditional particle filter algorithms for bearings-only tracking(BOT).This algorithm calculates the... In this study,a fifth-degree cubature particle filter(5CPF)is proposed to address the limited estimation accuracy in traditional particle filter algorithms for bearings-only tracking(BOT).This algorithm calculates the recommended density function by introducing a fifth-degree cubature Kalman filter algorithm to guide particle sampling,which effectively alleviates the problem of particle degradation and significantly improves the estimation accuracy of the filter.However,the 5CPF algorithm exhibits high computational complexity,particularly in scenarios with a large number of particles.Therefore,we propose the extended Kalman filter(EKF)-5CPF algorithm,which employs an EKF to replace the time update step for each particle in the 5CPF.This enhances the algorithm’s real-time capability while maintaining the high precision advantage of the 5CPF algorithm.In addition,we construct bearing-only dual-station and single-motion station target tracking systems,and the filtering performances of 5CPF and EKF-5CPF algorithms under different conditions are analyzed.The results show that both the 5CPF algorithm and EKF-5CPF have strong robustness and can adapt to different noise environments.Furthermore,both algorithms significantly outperform traditional nonlinear filtering algorithms in terms of convergence speed,tracking accuracy,and overall stability. 展开更多
关键词 Nonlinear filtering Fifth-degree cubature particle filter EKF-5CPF Bearings-only target motion analysis
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Cooperative localization for multi-airship navigation network with hybrid kernel particle filter 认领 引用
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作者 Ying MA Rui XUE +1 位作者 Xingzi QIANG Yingkui GONG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第5期48-66,共19页
Cooperative Localization(CL)enables agents to enhance their self-location accuracy by leveraging additional information from neighboring nodes.In near-space airship formations,CL facilitates the autonomous maintenance... Cooperative Localization(CL)enables agents to enhance their self-location accuracy by leveraging additional information from neighboring nodes.In near-space airship formations,CL facilitates the autonomous maintenance of spatiotemporal references.Particle Filters(PFs)are commonly employed to address CL challenges under nonlinear and non-Gaussian conditions.However,broadcasting redundant cooperative information in large networks leads to excessive observation dimensions.Additionally,unknown disturbances and anomalous observations introduce non-Gaussian noise.These factors lead to weight degeneracy in PFs,degrading positioning accuracy.This paper proposes a novel game-theoretic CL mechanism specifically tailored for near-space airships.Our mechanism integrates a perception and strategy selection method to select collaborative nodes with higher positioning accuracy,along with a robust distributed hybrid kernel PF to mitigate non-Gaussian noise.The perception and strategy selection method is designed based on the heterogeneous investment public goods game,for which the benefit function is constructed using the Cramer–Rao lower bound to allocate more investment to nodes exhibiting superior accuracy.The distributed hybrid kernel PF optimizes the proposal distribution through adaptive important region sampling and mean-shift migration,effectively managing noise uncertainty.Simulation experiments on a two-layer network of 43 airships demonstrate that our algorithm selects optimal measurements to reduce redundancy while preserving accuracy.The results highlight improvements in positioning and timing accuracy under different noise conditions compared with other methods. 展开更多
关键词 Multi-airship network Cooperative localization Global Navigation Satellite System Particle filter Game theory
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Robust particle filtering-based nonlinear model predictive control:Application to PEMFC process 认领 引用
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作者 Qi Zhang Fanda Pan Lei Xie 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2025年第12期321-334,共14页
The application of plant measurement data for system identification and model predictive control(MPC)has garnered significant interest.However,the pervasive presence of noise and contamination in industrial data often... The application of plant measurement data for system identification and model predictive control(MPC)has garnered significant interest.However,the pervasive presence of noise and contamination in industrial data often compromises data quality,thereby degrading performance and reliability of model.To address this challenge,this study proposes a nonlinear MPC method based on robust time delay particle filtering(RPF-MPC).This method is specifically designed to mitigate the impact of stochastic time delays and noise on both model learning and control.RPF-MPC utilizes robust particle filtering with a Laplace distribution to reliably estimate parameters and unknown time delays.In this way,the controller is able to efficiently handle noise and outliers even when the data deviates from a Gaussian distribution.The proposed algorithm is presented in detail,a nonlinear numerical case and a fuel cell water cooling control case are presented to validate the effectiveness of the RPF-MPC method.Simulation results validate the effectiveness and robustness of the RPF-MPC method in handling uncertainty and improving control performance in the PEMFC process. 展开更多
关键词 System identification Model predictive control Particle filter Bayesian inference Time delay
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Improving the Position Accuracy and Computational Efficiency of UAV Terrain Aided Navigation Using a Two-Stage Hybrid Fuzzy Particle Filtering Method 认领 引用 被引量：1
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作者 Sofia Yousuf Muhammad Bilal Kadri 《Computers, Materials & Continua》 SCIE EI 2025年第1期1193-1210,共18页
Terrain Aided Navigation(TAN)technology has become increasingly important due to its effectiveness in environments where Global Positioning System(GPS)is unavailable.In recent years,TAN systems have been extensively r... Terrain Aided Navigation(TAN)technology has become increasingly important due to its effectiveness in environments where Global Positioning System(GPS)is unavailable.In recent years,TAN systems have been extensively researched for both aerial and underwater navigation applications.However,many TAN systems that rely on recursive Unmanned Aerial Vehicle(UAV)position estimation methods,such as Extended Kalman Filters(EKF),often face challenges with divergence and instability,particularly in highly non-linear systems.To address these issues,this paper proposes and investigates a hybrid two-stage TAN positioning system for UAVs that utilizes Particle Filter.To enhance the system’s robustness against uncertainties caused by noise and to estimate additional system states,a Fuzzy Particle Filter(FPF)is employed in the first stage.This approach introduces a novel terrain composite feature that enables a fuzzy expert system to analyze terrain non-linearities and dynamically adjust the number of particles in real-time.This design allows the UAV to be efficiently localized in GPS-denied environments while also reducing the computational complexity of the particle filter in real-time applications.In the second stage,an Error State Kalman Filter(ESKF)is implemented to estimate the UAV’s altitude.The ESKF is chosen over the conventional EKF method because it is more suitable for non-linear systems.Simulation results demonstrate that the proposed fuzzy-based terrain composite method achieves high positional accuracy while reducing computational time and memory usage. 展开更多
关键词 Sensor fusion fuzzy logic particle filter composite feature terrain aided navigation
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Multivariate Lithium-ion Battery State Prediction with Channel-Independent Informer and Particle Filter for Battery Digital Twin 认领 引用
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作者 Changyu Jeon Younghoon Kim 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第12期3723-3745,共23页
Accurate State-of-Health(SOH)prediction is critical for the safe and efficient operation of lithium-ion batteries(LiBs).However,conventional methods struggle with the highly nonlinear electrochemical dynamics and decl... Accurate State-of-Health(SOH)prediction is critical for the safe and efficient operation of lithium-ion batteries(LiBs).However,conventional methods struggle with the highly nonlinear electrochemical dynamics and declining accuracy over long-horizon forecasting.To address these limitations,this study proposes CIPF-Informer,a novel digital twin framework that integrates the Informer architecture with Channel Independence(CI)and a Particle Filter(PF).The CI mechanism enhances robustness by decoupling multivariate state dependencies,while the PF captures the complex stochastic variations missed by purely deterministic models.The proposed framework was evaluated using the Massachusetts Institute of Technology(MIT)battery dataset against benchmark deep learning models.Results demonstrate that CIPF-Informer consistently achieves superior performance,in multivariate and long sequence forecasting scenarios.By effectively synergizing a model-based method with a data-driven model,CIPF-Informer provides a more reliable pathway for advancing Battery Management System(BMS)technologies,contributing to the development of safer and more sustainable energy storage systems. 展开更多
关键词 Digital twin battery state prediction lithium-ion battery informer channel independence particle filter
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An unscented particle filter for ground maneuvering target tracking 认领 引用 被引量：6
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作者 GUO Rong-hua QIN Zheng 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS 2007年第10期1588-1595,共8页
In this study, an unscented particle filtering method based on an interacting multiple model (IMM) frame for a Markovian switching system is presented. The method integrates the multiple model (MM) filter with an unsc... In this study, an unscented particle filtering method based on an interacting multiple model (IMM) frame for a Markovian switching system is presented. The method integrates the multiple model (MM) filter with an unscented particle filter (UPF) by an interaction step at the beginning. The framework (interaction/mixing, filtering, and combination) is similar to that in a standard IMM filter, but an UPF is adopted in each model. Therefore, the filtering performance and degeneracy phenomenon of particles are improved. The filtering method addresses nonlinear and/or non-Gaussian tracking problems. Simulation results show that the method has better tracking performance compared with the standard IMM-type filter and IMM particle filter. 展开更多
关键词 Interacting multiple model （IMM） Unscented particle filter （UPF） Ground target tracking Particle filter （PF）
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An Improved Gaussian Particle Filter Algorithm Using KLD-Sampling 认领 引用 被引量：1
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作者 ZHOU Zhaihe ZHONG Yulu +1 位作者 ZENG Qingxi TIAN Xiangrui 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2020年第4期607-614,共8页
To adjust the samples of filtering adaptively,an improved Gaussian particle filter algorithm based on Kullback-Leibler divergence(KLD)-sampling(KLGPF)is proposed in this paper.During the process of sampling,the algori... To adjust the samples of filtering adaptively,an improved Gaussian particle filter algorithm based on Kullback-Leibler divergence(KLD)-sampling(KLGPF)is proposed in this paper.During the process of sampling,the algorithm calculates the KLD to adjust the size of the particle set between the discrete probability density function of particles and the true posterior probability density function.KLGPF has significant effect when the noise obeys Gaussian distribution and the statistical characteristics of noise change abruptly.Simulation results show that KLGPF could maintain a good estimation effect when the noise statistics changes abruptly.Compared with the particle filter algorithm using KLD-sampling(KLPF),the speed of KLGPF increases by 28%under the same conditions. 展开更多
关键词 particle filter Gaussian particle filter KLD-sampling noise mutation adaptive particle numbers
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Passive target tracking using marginalized particle filter 认领 引用 被引量：1
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作者 Zhan Ronghui Wang Ling Wan Jianwei Sun Zhongkang 《Journal of Systems Engineering and Electronics》 SCIE EI 2007年第3期503-508,共6页
A marginalized particle filtering （MPF） approach is proposed for target tracking under the background of passive measurement. Essentially, the MPF is a combination of particle filtering technique and Kalman filter. ... A marginalized particle filtering （MPF） approach is proposed for target tracking under the background of passive measurement. Essentially, the MPF is a combination of particle filtering technique and Kalman filter. By making full use of marginalization, the distributions of the tractable linear part of the total state variables are updated analytically using Kalman filter, and only the lower-dimensional nonlinear state variable needs to be dealt with using particle filter. Simulation studies are performed on an illustrative example, and the results show that the MPF method leads to a significant reduction of the tracking errors when compared with the direct particle implementation. Real data test results also validate the effectiveness of the presented method. 展开更多
关键词 nonlinear filtering passive target tracking particle filter marginalized particle filter state estimation.
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Particle Filter for Estimation and Tracking 认领 引用
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作者 王继贞 赵增顺 +1 位作者 安笑伟 田淑霞 《Journal of Measurement Science and Instrumentation》 CAS 2010年第3期267-270,共4页
In recent years, the particle filter technique has been widely used in tracking, estimation and navigation. In this paper, the authors described several practical filters including the general practical, the extended ... In recent years, the particle filter technique has been widely used in tracking, estimation and navigation. In this paper, the authors described several practical filters including the general practical, the extended Kaman practical, and the unsented particle filters. And they explained the degeneracy problem in the practical filter protess, and introduced some solved methods. Finally they demonstrated the estimation of different particle filters in non-liner and non-Gaussian situation respectively. The result proved the unscented particle filter had the best performance. 展开更多
关键词 particle filter extended Kaman practical filter unscented particle filter
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The statistical observation localized equivalent-weights particle filter in a simple nonlinear model 认领 引用 被引量：4
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作者 Yuxin Zhao Shuo Yang +4 位作者 Renfeng Jia Di Zhou Xiong Deng Chang Liu Xinrong Wu 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2022年第2期80-90,共11页
This paper presents an improved approach based on the equivalent-weights particle filter(EWPF)that uses the proposal density to effectively improve the traditional particle filter.The proposed approach uses historical... This paper presents an improved approach based on the equivalent-weights particle filter(EWPF)that uses the proposal density to effectively improve the traditional particle filter.The proposed approach uses historical data to calculate statistical observations instead of the future observations used in the EWPF’s proposal density and draws on the localization scheme used in the localized PF(LPF)to construct the localized EWPF.The new approach is called the statistical observation localized EWPF(LEWPF-Sobs);it uses statistical observations that are better adapted to the requirements of real-time assimilation and the localization function is used to calculate weights to reduce the effect of missing observations on the weights.This approach not only retains the advantages of the EWPF,but also improves the assimilation quality when using sparse observations.Numerical experiments performed with the Lorenz 96 model show that the statistical observation EWPF is better than the EWPF and EAKF when the model uses standard distribution observations.Comparisons of the statistical observation localized EWPF and LPF reveal the advantages of the new method,with fewer particles giving better results.In particular,the new improved filter performs better than the traditional algorithms when the observation network contains densely spaced measurements associated with model state nonlinearities. 展开更多
关键词 data assimilation particle filter equivalent weights particle filter localization methods
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State Estimation of Drive-by-Wire Chassis Vehicle Based on Dual Unscented Particle Filter Algorithm 认领 引用 被引量：3
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作者 Zixu Wang Chaoning Chen +2 位作者 Quan Jiang Hongyu Zheng Chuyo Kaku 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2024年第1期99-113,共15页
Accurate vehicle dynamic information plays an important role in vehicle driving safety.However,due to the characteristics of high mobility and multiple controllable degrees of freedom of drive-by-wire chassis vehicles... Accurate vehicle dynamic information plays an important role in vehicle driving safety.However,due to the characteristics of high mobility and multiple controllable degrees of freedom of drive-by-wire chassis vehicles,the current mature application of traditional vehicle state estimation algorithms can not meet the requirements of drive-by-wire chassis vehicle state estimation.This paper proposes a state estimation method for drive-by-wire chassis vehicle based on the dual unscented particle filter algorithm,which make full use of the known advantages of the four-wheel drive torque and steer angle parameters of the drive-by-wire chassis vehicle.In the dual unscented particle filter algorithm,two unscented particle filter transfer information to each other,observe the vehicle state information and the tire force parameter information of the four wheels respectively,which reduce the influence of parameter uncertainty and model parameter changes on the estimation accuracy during driving.The performance with the dual unscented particle filter algorithm,which is analyzed in terms of the time-average square error,is superior of the unscented Kalman filter algorithm.The effectiveness of the algorithm is further verified by driving simulator test.In this paper,a vehicle state estimator based on dual unscented particle filter algorithm was proposed for the first time to improve the estimation accuracy of vehicle parameters and states. 展开更多
关键词 Drive-by-wire chassis vehicle Vehicle state estimation Dual unscented particle filter Tire force estimation Unscented particle filter
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Remaining useful life prediction of aircraft lithium-ion batteries based on F-distribution particle filter and kernel smoothing algorithm 认领 引用 被引量：11
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作者 Kai ZHANG Peng ZHAO +2 位作者 Canfei SUN Youren WANG Zewang CHEN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2020年第5期1517-1531,共15页
As an emergency and auxiliary power source for aircraft,lithium(Li)-ion batteries are important components of aerospace power systems.The Remaining Useful Life(RUL)prediction of Li-ion batteries is a key technology to... As an emergency and auxiliary power source for aircraft,lithium(Li)-ion batteries are important components of aerospace power systems.The Remaining Useful Life(RUL)prediction of Li-ion batteries is a key technology to ensure the reliable operation of aviation power systems.Particle Filter(PF)is an effective method to predict the RUL of Li-ion batteries because of its uncertainty representation and management ability.However,there are problems that particle weights cannot be updated in the prediction stage and particles degradation.To settle these issues,an innovative technique of F-distribution PF and Kernel Smoothing(FPFKS)algorithm is proposed.In the prediction stage,the weights of the particles are dynamically updated by the F kernel instead of being fixed all the time.Meanwhile,a first-order independent Markov capacity degradation model is established.Moreover,the kernel smoothing algorithm is integrated into PF,so that the variance of the parameters of capacity degradation model keeps invariant.Experiments based on NASA battery data sets show that FPFKS can be excellently applied to RUL prediction of Liion batteries. 展开更多
关键词 F-distribution Kernel smoothing Lithium-ion batteries Markov model Particle filter Prediction Remaining useful life
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A direct position determination method with combined TDOA and FDOA based on particle filter 认领 引用 被引量：18
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作者 Zhiyu LU Bin BA +2 位作者 Jianhui WANG Wenchao LI Daming WANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2018年第1期161-168,共8页
The localization of a stationary transmitter using moving receivers is considered. The original Direct Position Determination （DPD） methods, with combined Time Difference of Arrival （TDOA） and Frequency Difference... The localization of a stationary transmitter using moving receivers is considered. The original Direct Position Determination （DPD） methods, with combined Time Difference of Arrival （TDOA） and Frequency Difference of Arrival （FDOA）, do not perform well under low Signal-to-Noise Ratio （SNR）, and worse still, the computation cost is difficult to accept when the computational capabilities are limited. To get better positioning performance, we present a new DPD algorithm that proves to be more computationally efficient and more precise for weak signals than the conventional approach. The algorithm partitions the signal received with the same receiver into multiple non-overlapping short-time signal segments, and then uses the TDOA, the FDOA and the coherency among the short-time signals to locate the target. The fast maximum likelihood estimation, one iterative method based on particle filter, is designed to solve the problem of high computation load. A secondary but important result is a derivation of closed-form expressions of the Cramer-Rao Lower Bound （CRLB）. The simulation results show that the algorithm proposed in this paper outperforms the traditional DPD algorithms with more accurate results and higher computational efficiency, and especially at low SNR, it is more close to the CRLB. 展开更多
关键词 Direct position determination Cramer-Rao lower bound Frequency difference of arrival Time difference of arrival Particle filter
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Federated unscented particle filtering algorithm for SINS/CNS/GPS system 认领 引用 被引量：13
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作者 胡海东 黄显林 +1 位作者 李明明 宋卓越 《Journal of Central South University》 SCIE EI CAS 2010年第4期778-785,共8页
To solve the problem of information fusion in the strapdown inertial navigation system(SINS)/celestial navigation system(CNS)/global positioning system(GPS) integrated navigation system described by the nonlinearon-Ga... To solve the problem of information fusion in the strapdown inertial navigation system(SINS)/celestial navigation system(CNS)/global positioning system(GPS) integrated navigation system described by the nonlinearon-Gaussian error models,a new algorithm called the federated unscented particle filtering(FUPF) algorithm was introduced.In this algorithm,the unscented particle filter(UPF) served as the local filter,the federated filter was used to fuse outputs of all local filters,and the global filter result was obtained.Because the algorithm was not confined to the assumption of Gaussian noise,it was of great significance to integrated navigation systems described by the non-Gaussian noise.The proposed algorithm was tested in a vehicle's maneuvering trajectory,which included six flight phases:climbing,level flight,left turning,level flight,right turning and level flight.Simulation results are presented to demonstrate the improved performance of the FUPF over conventional federated unscented Kalman filter(FUKF).For instance,the mean of position-error decreases from(0.640×10-6 rad,0.667×10-6 rad,4.25 m) of FUKF to(0.403×10-6 rad,0.251×10-6 rad,1.36 m) of FUPF.In comparison of the FUKF,the FUPF performs more accurate in the SINS/CNS/GPS system described by the nonlinearon-Gaussian error models. 展开更多
关键词 navigation system integrated navigation unscented Kalman filter unscented particle filter
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The Marginal Rao-Blackwellized Particle Filter for Mixed Linear/Nonlinear State Space Models 认领 引用 被引量：18
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作者 Yin Jianjun Zhang Jianqiu Mike Klaas 《Chinese Journal of Aeronautics》 SCIE EI CAS 2007年第4期346-352,共7页
In this paper, the marginal Rao-Blackwellized particle filter （MRBPF）, which fuses the Rao-Blackwellized particle filter （RBPF） algorithm and the marginal particle filter （MPF） algorithm, is presented. The state... In this paper, the marginal Rao-Blackwellized particle filter （MRBPF）, which fuses the Rao-Blackwellized particle filter （RBPF） algorithm and the marginal particle filter （MPF） algorithm, is presented. The state space is divided into linear and non-linear parts, which can be estimated separately by the MPF and the optional Kalman filter. Through simulation in the terrain aided navigation （TAN） domain, it is demonstrated that, compared with the RBPF, the root mean square errors （RMSE） and the error variance of the nonlinear state estimations by the proposed MRBPF are respectively reduced by 29% and 96%, while the unique particle count is increased by 80%. It is also found that the MRBPF has better convergence properties, and analysis has shown that the existing RBPF is nothing more than a special case of the MRBPF. 展开更多
关键词 signal processing marginal Rao-Blackwellized particle filter simulation mixed linearonlinear terrain aided navigation
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A genetic resampling particle filter for freeway traffic-state estimation 认领 引用 被引量：11
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作者 毕军 关伟 齐龙涛 《Chinese Physics B》 SCIE EI CAS CSCD 2012年第6期595-599,共5页
On-line estimation of the state of traffic based on data sampled by electronic detectors is important for intelligent traffic management and control. Because a nonlinear feature exists in the traffic state, and becaus... On-line estimation of the state of traffic based on data sampled by electronic detectors is important for intelligent traffic management and control. Because a nonlinear feature exists in the traffic state, and because particle filters have good characteristics when it comes to solving the nonlinear problem, a genetic resampling particle filter is proposed to estimate the state of freeway traffic. In this paper, a freeway section of the northern third ring road in the city of Beijing in China is considered as the experimental object. By analysing the traffic-state characteristics of the freeway, the traffic is modeled based on the second-order validated macroscopic traffic flow model. In order to solve the particle degeneration issue in the performance of the particle filter, a genetic mechanism is introduced into the resampling process. The realization of a genetic particle filter for freeway traffic-state estimation is discussed in detail, and the filter estimation performance is validated and evaluated by the achieved experimental data. 展开更多
关键词 particle filter genetic mechanism traffic-state estimation traffic flow model
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Using interacting multiple model particle filter to track airborne targets hidden in blind Doppler 认领 引用 被引量：16
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作者 DU Shi-chuan SHI Zhi-guo +1 位作者 ZANG Wei CHEN Kang-sheng 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS 2007年第8期1277-1282,共6页
In airborne tracking,the blind Doppler makes the target undetectable,resulting in tracking difficulties. In this paper,we studied most possible blind-Doppler cases and summed them up into two types:targets' intent... In airborne tracking,the blind Doppler makes the target undetectable,resulting in tracking difficulties. In this paper,we studied most possible blind-Doppler cases and summed them up into two types:targets' intentional tangential flying to radar and unintentional flying with large tangential speed. We proposed an interacting multiple model(IMM) particle filter which combines a constant velocity model and an acceleration model to handle maneuvering motions. We compared the IMM particle filter with a previous particle filter solution. Simulation results showed that the IMM particle filter outperforms the method in previous works in terms of tracking accuracy and continuity. 展开更多
关键词 Interacting multiple model Particle filter Blind Doppler
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Vehicle State and Parameter Estimation Based on Dual Unscented Particle Filter Algorithm 认领 引用 被引量：11
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作者 林棻 王浩 +2 位作者 王伟 刘存星 谢春利 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2014年第5期568-575,共8页
Acquisition of real-time and accurate vehicle state and parameter information is critical to the research of vehicle dynamic control system.By studying the defects of the former Kalman filter based estimation method,a... Acquisition of real-time and accurate vehicle state and parameter information is critical to the research of vehicle dynamic control system.By studying the defects of the former Kalman filter based estimation method,a new estimating method is proposed.First the nonlinear vehicle dynamics system,containing inaccurate model parameters and constant noise,is established.Then a dual unscented particle filter(DUPF)algorithm is proposed.In the algorithm two unscented particle filters run in parallel,states estimation and parameters estimation update each other.The results of simulation and vehicle ground testing indicate that the DUPF algorithm has higher state estimation accuracy than unscented Kalman filter(UKF)and dual extended Kalman filter(DEKF),and it also has good capability to revise model parameters. 展开更多
关键词 vehicle dynamics dual unscented particle filter(DUPF) state estimation virtual experiment
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Fault detection and identification for dead reckoning system of mobile robot based on fuzzy logic particle filter 认领 引用 被引量：5
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作者 余伶俐 蔡自兴 +1 位作者 周智 奉振球 《Journal of Central South University》 SCIE EI CAS 2012年第5期1249-1257,共9页
To deal with fault detection and diagnosis with incomplete model for dead reckoning system of mobile robot,an integrative framework of particle filter detection and fuzzy logic diagnosis was devised.Firstly,an adaptiv... To deal with fault detection and diagnosis with incomplete model for dead reckoning system of mobile robot,an integrative framework of particle filter detection and fuzzy logic diagnosis was devised.Firstly,an adaptive fault space is designed for recognizing both known faults and unknown faults,in corresponding modes of modeled and model-free.Secondly,the particle filter is utilized to diagnose the modeled faults and detect model-free fault according to the low particle weight and reliability.Especially,the proposed fuzzy logic diagnosis can further analyze model-free modes and identify some soft faults in unknown fault space.The MORCS-1 experimental results show that the fuzzy diagnosis particle filter(FDPF) combinational framework improves fault detection and identification completeness.Specifically speaking,FDPF is feasible to diagnose the modeled faults in known space.Furthermore,the types of model-free soft faults can also be further identified and diagnosed in unknown fault space. 展开更多
关键词 fault detection and diagnosis particle filter fuzzy logic hard fault soft fault
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Hybrid particle filtering algorithm for GPS multipath mitigation 认领 引用 被引量：5
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作者 郑南山 蔡良师 +1 位作者 卞和方 林聪 《Transactions of Nonferrous Metals Society of China》 SCIE EI CAS CSCD 2014年第5期1554-1561,共8页
An altemative algorithm for mitigating GPS multipath was presented by integrating unscented Kalman filter （UKF） and wavelet transform with particle filter. Within consideration of particle degeneracy, UKF was taken ... An altemative algorithm for mitigating GPS multipath was presented by integrating unscented Kalman filter （UKF） and wavelet transform with particle filter. Within consideration of particle degeneracy, UKF was taken for drawing particle. To remove the noise from raw data and data processing error, adaptive wavelet filtering with threshold was adopted while data preprocessing and drawing particle. Three algorithms, named EKF-PF, UKF-PF and WM-UKF-PF, were performed for comparison. The proposed WM-UKF-PF algorithm gives better error minimization, and significantly improves performance of multipath mitigation in terms of SNR and coefficient even though it has computation complexity. It is of significance for high-accuracy positioning and non-stationary deformation analysis. 展开更多
关键词 particle filtering wavelet transformation global positioning system （GPS） multipath mitigation
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