中国科学院机构知识库网格系统: 检索

A Novel Adaptive Kalman Filter Based on Credibility Measure 期刊论文 OAI收割
IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 1, 页码: 103-120
作者: Quanbo Ge; Xiaoming Hu; Yunyu Li; Hongli He; Zihao Song

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收藏 | 浏览/下载：51/0 | 提交时间：2023/01/03

Credibility expectation maximization-particle swarm optimization method (EM-PSO) filter calculated mean square errors (MSE) inaccurate models Kalman filter Sage-Husa true MSE (TMSE)

Relative closeness ranking of Kalman filtering with multiple mismatched measurement noise covariances 期刊论文 OAI收割
IET CONTROL THEORY AND APPLICATIONS, 2018, 卷号: 12, 期号: 8, 页码: 1133-1140
作者: Shao, Teng; Ge, Quanbo; Duan, Zhansheng; Yu, Junzhi

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收藏 | 浏览/下载：32/0 | 提交时间：2018/10/10

Linear Systems Kalman Filters Mean Square Error Methods Relative Closeness Ranking Kalman Filtering Multiple Mismatched Measurement Noise Covariances Linear Dynamic Systems Ideal Mse Filter Calculated Mse True Mse Fmse Tmse Imse Mean Squared Error

ANALYSIS OF NORMALIZED LEAST MEAN SQUARES-BASED CONSENSUS ADAPTIVE FILTERS UNDER A GENERAL INFORMATION CONDITION 期刊论文 OAI收割
SIAM JOURNAL ON CONTROL AND OPTIMIZATION, 2018, 卷号: 56, 期号: 5, 页码: 3404-3431
作者: Xie, Siyu; Guo, Lei

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收藏 | 浏览/下载：18/0 | 提交时间：2019/01/11

distributed adaptive filter consensus algorithm least mean squares information condition stochastic stability tracking performance

A General Zero Attraction Proportionate Normalized Maximum Correntropy Criterion Algorithm for Sparse System Identification 期刊论文 OAI收割
SYMMETRY-BASEL, 2017, 卷号: 9, 期号: 10, 页码: 229
作者: Li, Yingsong; Wang, Yanyan; Albu, Felix; Jiang, Jingshan; Li, YS (reprint author), Harbin Engn Univ, Coll Informat & Commun Engn, Harbin 150001, Heilongjiang, Peoples R China.

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收藏 | 浏览/下载：30/0 | 提交时间：2017/12/19

Adaptive Filter Normalized Maximum Correntropy Criterion Normalized Least Mean Square (Nlms) Proportionate Nlms (pNlms) Zero Attracting Impulsive Noise

Efficient and Robust Learning for Sustainable and Reacquisition-Enabled Hand Tracking 期刊论文 OAI收割
ieee transactions on cybernetics, 2016, 卷号: 46, 期号: 4, 页码: 945-958
作者: Aziz, Muhammad Ali Abdul; Niu, Jianwei; Zhao, Xiaoke; Li, Xuelong

收藏 | 浏览/下载：32/0 | 提交时间：2016/08/18

Computer vision histograms of oriented gradient (HOG) local binary pattern (LBP) machine learning mean shift implanted particle filter

Goce mean dynamic topography and its associated geostrophic current based on the regional filtering method 期刊论文 iSwitch采集
Chinese journal of geophysics-chinese edition, 2015, 卷号: 58, 期号: 5, 页码: 1535-1546
作者: Bai Xi-Xuan; Yan Hao-Ming; Zhu Yao-Zhong; Peng Peng

收藏 | 浏览/下载：29/0 | 提交时间：2019/05/10

Mean dynamic ocean topography (mdt) Geostrophic current velocity Spectral-wise method Buoys Filter radius

Target tracking based on non-linear kernel density estimation and Kalman filter 会议论文 OAI收割
2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), Shenyang, China, June 8-12, 2015
作者: Wu Y(吴阳); Zhou XF(周晓锋); Zhang YC(张宜弛)

收藏 | 浏览/下载：20/0 | 提交时间：2016/04/30

target tracking non-linear kernel density estimation Mean Shift Kalman filter

Hypersepctral Imagery Compression via RLS filter 期刊论文 OAI收割
Electronics Letters, 2013, 卷号: 21, 期号: 8, 页码: 5345-5351
Jinwei SONG; Zhongwei ZHANG; Xiaomin CHEN

收藏 | 浏览/下载：32/0 | 提交时间：2014/04/30

Signed Least Mean Square Filter Multiband Lookup Tables Hyperspectral Imagery Compression

Analysis on the influence of random vibration on MEMS gyro precision and error compensation (EI CONFERENCE) 会议论文 OAI收割
2011 3rd International Conference on Mechanical and Electronics Engineering, ICMEE 2011, September 23, 2011 - September 25, 2011, Hefei, China
作者: Li M.

收藏 | 浏览/下载：22/0 | 提交时间：2013/03/25

In order to improve its precision in dynamic environment a Kalman filter was designed. Firstly two sets of random drift data of MEMS gyro were respectively analysed and it was found that the variance of random drift under random vibration significantly increased and its mean also changed. Then calculation results show that attitude angle error under random vibration is 2.6 while in the static test it is 0.25. Analysis on the characteristics of random drift was carried out and it is found that it can be treated as stable normally distributed random signal. Finally a corresponding Kalman filter was designed. The results indicated that after filtering the variance of random drift is reduced to 0.0282 26.4% of pre-filtering and the attitude angle error is reduced to 1.5 57.7% of pre-filtering. The above method can effectively compensate for the attitude angle error of MEMS gyro caused by random vibration. This study can be a reference to the application of low-cost MEMS gyro in aircraft navigation. (2012) Trans Tech Publications Switzerland.

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

收藏 | 浏览/下载：59/0 | 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).