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浏览/检索结果: 共24条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 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   |  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)   Phase Offset Tracking for Free Space Digital Coherent Optical Communication System 期刊论文  OAI收割 APPLIED SCIENCES-BASEL, 2019, 卷号: 9, 期号: 5 作者:  Li, Hongwei;  Huang, Yongmei;  Wang, Qiang;  He, Dong;  Peng, Zhenming   |  收藏  |  浏览/下载：23/0  |  提交时间：2021/05/06 phase offset  estimation method  Viterbi-Viterbi algorithm  Kalman filter  atmosphere turbulence  estimation error   A multi-mode real-time terrain parameter estimation method for wheeled motion control of mobile robots 期刊论文  OAI收割 MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2018, 卷号: 104, 页码: 758-775 作者:  Li, Yuankai;  Ding, Liang;  Zheng, Zhizhong;  Yang, Qizhi;  Zhao, Xingang   |  收藏  |  浏览/下载：24/0  |  提交时间：2021/02/02 Terrain parameters  Real-time estimation  Multi-mode  recursive Gauss-Newton method  adaptive robust extended Kalman filter   A multi-mode real-time terrain parameter estimation method for wheeled motion control of mobile robots 期刊论文  OAI收割 Mechanical Systems and Signal Processing, 2018, 卷号: 104, 页码: 758-775 作者:  Liu GJ(刘光军);  Li YK(李元凯);  Zheng, Zhizhong;  Ding, Liang;  Yang, Qizhi   |  收藏  |  浏览/下载：59/0  |  提交时间：2018/01/06 Terrain parameters  Real-time estimation  Multi-mode  recursive Gauss-Newton method  adaptive robust extended Kalman filter   Fusion of Vision and IMU to track the racket trajectory in real time 会议论文  OAI收割 Takamatsu, Japan, 6-9 Aug. 2017 作者:  Zhang K(张鵾);  Fang Zaojun;  Liu Jianran;  Wu Zhengxing;  Tan Min   |  收藏  |  浏览/下载：31/0  |  提交时间：2018/06/08 Racket Pose  Vision And Imu Sensors  Nonlinear Method  Extended Kalman Filter   Retrieval of leaf area index using temporal, spectral, and angular information from multiple satellite data SCI/SSCI论文  OAI收割 2014 Liu Q.; Liang S. L.; Xiao Z. Q.; Fang H. L. 收藏  |  浏览/下载：26/0  |  提交时间：2014/12/24 Leaf area index  Multiple sensors  Ensemble Kalman filter  Iterative  method  cyclopes global products  canopy reflectance model  foliage clumping  index  time-series  in-situ  hemispherical photography  atmosphere  interactions  vegetation indexes  part 1  modis   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：23/0  |  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 study of two FOG filter methods in improving the precision of servo control system (EI CONFERENCE) 会议论文  OAI收割 2010 3rd International Conference on Advanced Computer Theory and Engineering, ICACTE 2010, August 20, 2010 - August 22, 2010, Chengdu, China 作者:  Zhang Y.;  Zhang L.-G.;  Zhang L.-G. 收藏  |  浏览/下载：19/0  |  提交时间：2013/03/25 The precision of FOG has highly effect on the servo system's final precision. In this paper  two filter methods has been researched in signal processing of FOG  smoothing filter method and Kalman filter based on ARMA model  in order to improve turn table servo control system's performance. To validate the effects of these two methods  three experiments have been made  which are filter examinations  dynamic experiments and Static experiments. The experiments reveal that these two kinds of filter methods are useful to filter the unknown noises and very effective to improve the precision of servo control system. 2010 IEEE.   Kalman filter for pointing deviation delay compensation in a TV tracker (EI CONFERENCE) 会议论文  OAI收割 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, CMCE 2010, August 24, 2010 - August 26, 2010, Changchun, China Juan C.; Wang Q.-P.; Jian C. 收藏  |  浏览/下载：23/0  |  提交时间：2013/03/25 Considering the pointing deviation delay in the TV tracker  which heavily worsens the tracking precision  here we offer an improved Kalman filter algorithm to compensate the delay. The output speed from the filter is fed forward to the speed loop for the compound control to increase the speed constant. The simulation results based on the experiment data show that the method can effectively estimate the position  consequently the speed and accelerate and the tracking precision is improved. The estimation precision of the speed and accelerate depend on that if the state model is coherent with the movement of the real target. 2010 IEEE.   Application of adaptive Kalman filter technique in initial alignment of strapdown inertial navigation system (EI CONFERENCE) 会议论文  OAI收割 29th Chinese Control Conference, CCC'10, July 29, 2010 - July 31, 2010, Beijing, China 作者:  Liu P. 收藏  |  浏览/下载：28/0  |  提交时间：2013/03/25 In order to improve the alignment precision and convergence speed of strap-down inertial navigation system  but in the active system most noise statistical characteristics are unknown  an initial alignment method based on Sage-Husa adaptive filter is presented. We also derived the exactitude alignment error model and adaptive Kalman filter equation in the azimuth of small misalignment angle. As usual  in this case  known the noise statistical characteristics  we introduce the adaptive Kalman filter. It uses the information of observed data  Kalman filter is suitable  on-line estimation noise statistical characteristics and state simultaneously in order to improve the filter continuously  so  the filter has a higher estimation accuracy than the conventional Kalman filter. By simulating verifying  the adaptive Kalman filter enhances the convergence speed and alignment accuracy effectively.