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浏览/检索结果: 共14条，第1-10条 帮助

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Attitude Decoupling Control of Semifloating Space Robots Using Time-Delay Estimation and Supertwisting Control 期刊论文  OAI收割
IEEE Transactions on Aerospace and Electronic Systems, 2021, 卷号: 57, 期号: 6, 页码: 4280-4295
作者:  Zhang X(张鑫);  Liu JG(刘金国);  Tong YC(佟玉闯);  Liu YW(刘玉旺)
  |  收藏  |  浏览/下载：43/0  |  提交时间：2021/08/21
GNSS time offset monitoring based on the single difference among systems 期刊论文  OAI收割
IET RADAR SONAR AND NAVIGATION, 2020, 卷号: 14, 期号: 2, 页码: 299-302
作者:  Tu, Rui;  Zhang, Pengfei;  Zhang, Rui;  Fan, Lihong;  Liu, Jinhai
  |  收藏  |  浏览/下载：26/0  |  提交时间：2020/11/09
Adaptive robust decoupling control of multi-arm space robots using time-delay estimation technique 期刊论文  OAI收割
NONLINEAR DYNAMICS, 2020, 卷号: 100, 期号: 3, 页码: 2449–2467
作者:  Zhang X(张鑫);  Liu JG(刘金国);  Gao Q(高庆)
  |  收藏  |  浏览/下载：26/0  |  提交时间：2020/05/24
Time-varying state-space model identification of an on-orbit rigid-flexible coupling spacecraft using an improved predictor-based recursive subspace algorithm 期刊论文  OAI收割
Acta Astronautica, 2019, 卷号: 163, 页码: 157-167
作者:  Ni ZY(倪智宇);  Liu JG(刘金国);  Wu SN(邬树楠);  Wu ZG(吴志刚)
  |  收藏  |  浏览/下载：30/0  |  提交时间：2018/12/01
Hierarchical Bayesian space-time estimation of monthly maximum and minimum surface air temperature 期刊论文  OAI收割
REMOTE SENSING OF ENVIRONMENT, 2018, 卷号: 211, 页码: 48-58
作者:  Lu, Ning;  Liang, Shunlin;  Huang, Guanghui;  Qin, Jun;  Yao, Ling
  |  收藏  |  浏览/下载：29/0  |  提交时间：2019/05/23
Hierarchical Bayesian space-time estimation of monthly maximum and minimum surface air temperature 期刊论文  OAI收割
REMOTE SENSING OF ENVIRONMENT, 2018, 卷号: 211, 页码: 48-58
作者:  Lu, Ning;  Liang, Shunlin;  Huang, Guanghui;  Qin, Jun;  Yao, Ling
  |  收藏  |  浏览/下载：21/0  |  提交时间：2019/05/23
Hierarchical Bayesian space-time estimation of monthly maximum and minimum surface air temperature 期刊论文  OAI收割
REMOTE SENSING OF ENVIRONMENT, 2018, 卷号: 211, 页码: 48-58
作者:  Lu, Ning;  Liang, Shunlin;  Huang, Guanghui;  Qin, Jun;  Yao, Ling
  |  收藏  |  浏览/下载：19/0  |  提交时间：2019/05/23
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：63/0  |  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).  
Improved STAP algorithm based on APES 期刊论文  OAI收割
JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS, 2011, 卷号: 22, 期号: 3, 页码: 387-392
作者:  Zhang, Yan;  Zhang, Yunhua;  Gu, Xiang
收藏  |  浏览/下载：18/0  |  提交时间：2014/12/15
Space and time complexities and sensor threshold selection in quantized identification 期刊论文  OAI收割
AUTOMATICA, 2008, 卷号: 44, 期号: 12, 页码: 3014-3024
作者:  Wang, Le Yi;  Yin, G. George;  Zhang, Ji-Feng;  Zhao, Yanlong
  |  收藏  |  浏览/下载：57/0  |  提交时间：2018/07/30