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A model-based adaptive state of charge estimator for a lithium-ion battery using an improved adaptive particle filter 期刊论文
APPLIED ENERGY, 2017, 卷号: 190, 页码: 740-748
作者: Ye, Min; Guo, Hui; Cao, Binggang

收藏 | 浏览/下载：2/0 | 提交时间：2019/11/26

Lithium-ion battery Improved adaptive particle filter Electric vehicles State of charge Particle swarm filter

A ROBOT POSE ESTIMATION APPROACH BASED ON OBJECT TRACKING IN MONITORING SCENES 期刊论文
INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION, 2017, 卷号: 32, 期号: 3, 页码: 256-265
作者: Yuan, Wenbo; Cao, Zhiqiang; Zhang, Yujia; Tan, Min

收藏 | 浏览/下载：13/0 | 提交时间：2017/07/18

Object Tracking Improved Particle Filter Multi-mode Ransac Algorithm Pose Estimation Trajectory Fitting

Multiple-Object Tracking in Large-Scale Scene 期刊论文
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2016, 卷号: E99D, 期号: 7, 页码: 1903-1909
作者: Yuan, Wenbo; Cao, Zhiqiang; Tan, Min; Chen, Hongkai

收藏 | 浏览/下载：28/0 | 提交时间：2016/12/26

visual sensor network HOG improved particle filter re-identification object tracking

A remaining useful life prediction approach for lithium-ion batteries using Kalman filter and an improved particle filter 会议论文
2016 IEEE INTERNATIONAL CONFERENCE ON PROGNOSTICS AND HEALTH MANAGEMENT (ICPHM), 2016-01-01
作者: Mo, Baohua; Yu, Jingsong; Tang, Diyin; Liu, Hao

收藏 | 浏览/下载：6/0 | 提交时间：2019/12/30

lithium-ion battery remaining useful life capacity prediction Kalman filter improved particle filter

A remaining useful life prediction approach for lithium-ion batteries using Kalman filter and an improved particle filter 会议论文
IEEE International Conference on Prognostics and Health Management (ICPHM), Carleton Univ, Ottawa, CANADA, 2016-06-20
作者: Mo, Baohua; Yu, Jingsong; Tang, Diyin; Liu, Hao

收藏 | 浏览/下载：3/0 | 提交时间：2019/12/30

lithium-ion battery remaining useful life capacity prediction Kalman filter improved particle filter

A high precision particle filter based on improved differential evolution 期刊论文
Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University, 2014, 卷号: 48, 期号: 12, 页码: 1714-1720
作者: Cao, Jie; Li, Yu-Qin; Wu, Di

收藏 | 浏览/下载：26/0 | 提交时间：2020/11/14

Computer applications Evolutionary algorithms Health Monte Carlo methods Optimization Differential Evolution Fitness functions Improved differential evolutions Intelligent optimization Intelligent optimization algorithm Particle filter Real time performance Search strategies

An improved particle filter with applications in ballistic target tracking 期刊论文
Sensors and Transducers, 2014, 卷号: 172, 期号: [db:dc_citation_issue], 页码: 196-201
作者: Wu, Chun-Ling; Ju, Yong-Feng; Ju, Yong-Feng

收藏 | 浏览/下载：11/0 | 提交时间：2019/12/02

Ballistic target tracking Importance density function Improved particle filter Nonlinear filtering algorithm Particle filter Proposal distribution Quadrature Kalman filters Statistical linear regressions

Vehicle tracking based on multi-feature adaptive fusion 期刊论文
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery, 2013, 卷号: 44, 期号: 4, 页码: 33-38
作者: Li, Yuchen; Li, Zhanming

收藏 | 浏览/下载：19/0 | 提交时间：2020/11/14

Distribution functions Finite difference method Image processing Monte Carlo methods Tracking (position) Vehicles Improved particle filter Multi-feature fusion Observation information Particle degenerations Particle filter Proposal distribution Static and dynamic characteristics Vehicle tracking system

An improved particle filter algorithm and its performance analysis 期刊论文
Advances in Information Sciences and Service Sciences, 2012, 卷号: 4, 期号: 23, 页码: 547-555
作者: Li, Ming; He, Yong-Feng; Nian, Fu-Zhong

收藏 | 浏览/下载：71/0 | 提交时间：2020/11/14

Artificial fish swarm algorithms Chaotic search Improved particle filter Non-Gaussian systems Particle degradation Particle filter Particle filter algorithms Performance analysis

The new approach for infrared target tracking based on the particle filter algorithm (EI CONFERENCE) 会议论文
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, May 24, 2011 - May 24, 2011, Beijing, China
Sun H.; Han H.-X.

收藏 | 浏览/下载：50/0 | 提交时间：2013/03/25

Target tracking on the complex background in the infrared image sequence is hot research field. It provides the important basis in some fields such as video monitoring precision and video compression human-computer interaction. As a typical algorithms in the target tracking framework based on filtering and data connection the particle filter with non-parameter estimation characteristic have ability to deal with nonlinear and non-Gaussian problems so it were widely used. There are various forms of density in the particle filter algorithm to make it valid when target occlusion occurred or recover tracking back from failure in track procedure but in order to capture the change of the state space it need a certain amount of particles to ensure samples is enough and this number will increase in accompany with dimension and increase exponentially this led to the increased amount of calculation is presented. In this paper particle filter algorithm and the Mean shift will be combined. Aiming at deficiencies of the classic mean shift Tracking algorithm easily trapped into local minima and Unable to get global optimal under the complex background. From these two perspectives that "adaptive multiple information fusion" and "with particle filter framework combining" we expand the classic Mean Shift tracking framework.Based on the previous perspective we proposed an improved Mean Shift infrared target tracking algorithm based on multiple information fusion. In the analysis of the infrared characteristics of target basis Algorithm firstly extracted target gray and edge character and Proposed to guide the above two characteristics by the moving of the target information thus we can get new sports guide grayscale characteristics and motion guide border feature. Then proposes a new adaptive fusion mechanism used these two new information adaptive to integrate into the Mean Shift tracking framework. Finally we designed a kind of automatic target model updating strategy to further improve tracking performance. Experimental results show that this algorithm can compensate shortcoming of the particle filter has too much computation and can effectively overcome the fault that mean shift is easy to fall into local extreme value instead of global maximum value.Last because of the gray and fusion target motion information this approach also inhibit interference from the background ultimately improve the stability and the real-time of the target track. 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

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