期刊文献+

目标散布已知情况下鱼雷发现概率计算方法 被引量:9

Calculation method of find probability of moving search acoustic homing torpedo when probability density of target is known
在线阅读 下载PDF
导出
摘要 在目标散布概率密度已知的情况下,根据声自导鱼雷的搜索手段和机动搜索弹道,计算其相对于目标散布中心点的相对弹道。利用微积分原理,通过仿真方法计算积分区域,并对被积函数进行数值积分计算,求解声自导鱼雷机动搜索弹道发现目标的概率。 When probability density of target is known,according to the searching means and moving search trajectory of acoustic homing torpedo,calculate the relative trajectory to the center of target distributing area,using the principle of calculus,calculate the integral range through simulation,the probability density function is integrated by numerical method,thus the find probability is gained.
机构地区 海军潜艇学院
出处 《舰船科学技术》 北大核心 2013年第7期120-122,共3页 Ship Science and Technology
关键词 概率密度 机动搜索 积分区域 相对弹道 发现概率 probability density moving search integral region relative trajectory find probability
  • 相关文献

参考文献4

  • 1同济大学数学教研室.高等数学[M].北京:高等教育出版社,1991.320-322.
  • 2沈永欢 梁在中.实用数学手册[M].北京:科学出版社,2004..
  • 3朱清新.离散和连续空间中的最优搜索理论[M].北京:科学出版社,2006.12.
  • 4周斌,王军政,沈伟.基于全局概率密度搜索的快速目标跟踪[J].电子与信息学报,2010,32(11):2680-2685. 被引量:4

二级参考文献11

  • 1Maggio E and Cavallaro A. Accurate appearance based bayesian tracking for maneuvering targets [J]. Computer Vision and Image Understanding, 2009, 113: 544-555.
  • 2Zhang K, Kwok J T, and Tang M. Accelerate convergence using dynamic mean shift[C]. Proceedings of the 9th European Conference on Computer Vision. New York, USA, 2006: 257-268.
  • 3Fashing M and Tomasi C. Mean shift is a bound optimization[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(3): 471-474.
  • 4Shen C and Brooks M J. A fast global kernel density mode seeking with application to localization and tracking[C]. Proceedings of IEEE International Conference on Computer Vision. Los Alamitos, 2005: 1516-1523.
  • 5Yin Zhao-zheng and Collins R T. Object tracking and detection after occlusion via numerical hybrid local and global mode-seeking[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Anchorage, USA, 2008: 1-8.
  • 6Elgammal A and Duraiswami R. Probabilistic tracking in joint feature-spatial spaces[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Washington, D.C, USA, 2004: 790-797.
  • 7Comaniciu D and Meer P. Kernel-based object tracking [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(5): 564-577.
  • 8Carreira Perpinan M A. Acceleration Strategies for Gaussian Mean shift image segmentation[C]. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, 2006: 543-549.
  • 9王永忠,梁彦,赵春晖,潘泉.基于多特征自适应融合的核跟踪方法[J].自动化学报,2008,34(4):393-399. 被引量:57
  • 10姚红革,齐华,郝重阳.复杂情形下目标跟踪的自适应粒子滤波算法[J].电子与信息学报,2009,31(2):275-278. 被引量:9

共引文献21

同被引文献47

引证文献9

二级引证文献17

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部