3篇
您的检索式：作者名="Jin Yonggao"
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| 1 | HF radar sea clutter rejection by nonlinear projections显示文摘In the background of signal detection for high frequency (HF) radar, the sea clutter is quite significant and can mask some weak target signals. A new clutter rejection method named “nonlinear projection” is given to improve the SNR of the target. This approach is based on the recent observation that HF sea clutter may be modeled as a nonlinear deterministic dynamical system. After approximating the multidimensional reconstruction of the clutter by a lowdimensional attractor, projections onto this attractor can separate the clutter from other components. Real sea clutter, simulated target data and real target data are used to show that a nonlinear clutter rejection method is a promising technique to suppress sea clutter and enhances target detection. | Zhou Gongjian Jin Yonggao Dong Huachun Quan Taifan | 2005 | Journal of Systems Engineering and Electronics2005，16，4： | 3 |
| 2 | Particle filter initialization in non-linear non-Gaussian radar target tracking显示文摘When particle filter is applied in radar target tracking,the accuracy of the initial particles greatly effects the results of filtering.For acquiring more accurate initial particles,a new method called'competition strategy algorithm'is presented.In this method,initial measurements give birth to several particle groups around them,regularly.Each of the groups is tested several times,separately,in the beginning periods,and the group that has the most number of efficient particles is selected as the initial particles.For this method,sample initial particles selected are on the basis of several measurements instead of only one first measurement,which surely improves the accuracy of initial particles.The method sacrifices initialization time and computation cost for accuracy of initial particles.Results of simulation show that it greatly improves the accuracy of initial particles,which makes the effect of filtering much better. | Wang Jian Jin Yonggao Dai Dingzhang Dong Huachun Quan Taifan | 2007 | Journal of Systems Engineering and Electronics2007，18，3： | 2 |
| 3 | Uncertain information fusion with robust adaptive neural networks-fuzzy reasoning显示文摘In practical multi-sensor information fusion systems, there exists uncertainty about the network structure, active state of sensors, and information itself (including fuzziness, randomness, incompleteness as well as roughness, etc). Hence it requires investigating the problem of uncertain information fusion. Robust learning algorithm which adapts to complex environment and the fuzzy inference algorithm which disposes fuzzy information are explored to solve the problem. Based on the fusion technology of neural networks and fuzzy inference algorithm, a multi-sensor uncertain information fusion system is modeled. Also RANFIS learning algorithm and fusing weight synthesized inference algorithm are developed from the ANFIS algorithm according to the concept of robust neural networks. This fusion system mainly consists of RANFIS confidence estimator, fusing weight synthesized inference knowledge base and weighted fusion section. The simulation result demonstrates that the proposed fusion model and algorithm have the capability of uncertain information fusion, thus is obviously advantageous compared with the conventional Kalman weighted fusion algorithm. | Zhang Yinan Sun Qingwei Quan He Jin Yonggao Quan Taifan | 2006 | Journal of Systems Engineering and Electronics2006，17，3： | 2 |
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