改进cell密度聚类算法在空战目标分群中的应用
V247; 针对传统聚类算法对流形分布数据聚类效果差,且实时性不高的缺点,提出改进基于cell的密度聚类(Cell-Based density Spatial Clustering of Applications with Noise,CBSCAN)算法解决实时空战目标分群问题.通过分析空战态势参数,建立了空战目标分群通用模型,将目标分群转化为聚类问题.通过改进CBSCAN算法的簇类扩展方式,建立基于改进CBSCAN算法的目标分群模型.通过仿真实验,对比分析了K-means、最大期望算法、密度峰值算法、密度聚类算法、CBSCAN算法和改进CBSCAN算法在30种作战态势下的分群准确性和实时性,...
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Published in | 国防科技大学学报 Vol. 43; no. 4; pp. 108 - 117 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | Chinese |
Published |
空军工程大学空管领航学院,陕西西安 710051%中国人民解放军94994部队,江苏南京 210019%中国人民解放军94701部队,安徽安庆 246000%中国人民解放军94347部队,辽宁沈阳 110042
28.08.2021
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Subjects | |
Online Access | Get full text |
ISSN | 1001-2486 |
DOI | 10.11887/j.cn.202104014 |
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Abstract | V247; 针对传统聚类算法对流形分布数据聚类效果差,且实时性不高的缺点,提出改进基于cell的密度聚类(Cell-Based density Spatial Clustering of Applications with Noise,CBSCAN)算法解决实时空战目标分群问题.通过分析空战态势参数,建立了空战目标分群通用模型,将目标分群转化为聚类问题.通过改进CBSCAN算法的簇类扩展方式,建立基于改进CBSCAN算法的目标分群模型.通过仿真实验,对比分析了K-means、最大期望算法、密度峰值算法、密度聚类算法、CBSCAN算法和改进CBSCAN算法在30种作战态势下的分群准确性和实时性,结果表明:改进CBSCAN算法可以在编队数目未知和目标流形分布的条件下,对多目标编队进行正确分群,且实时性较原始算法提高约30%,具有实际应用价值. |
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AbstractList | V247; 针对传统聚类算法对流形分布数据聚类效果差,且实时性不高的缺点,提出改进基于cell的密度聚类(Cell-Based density Spatial Clustering of Applications with Noise,CBSCAN)算法解决实时空战目标分群问题.通过分析空战态势参数,建立了空战目标分群通用模型,将目标分群转化为聚类问题.通过改进CBSCAN算法的簇类扩展方式,建立基于改进CBSCAN算法的目标分群模型.通过仿真实验,对比分析了K-means、最大期望算法、密度峰值算法、密度聚类算法、CBSCAN算法和改进CBSCAN算法在30种作战态势下的分群准确性和实时性,结果表明:改进CBSCAN算法可以在编队数目未知和目标流形分布的条件下,对多目标编队进行正确分群,且实时性较原始算法提高约30%,具有实际应用价值. |
Author | 王新 闫孟达 嵇慧明 左家亮 尚金祥 杨任农 |
AuthorAffiliation | 空军工程大学空管领航学院,陕西西安 710051%中国人民解放军94994部队,江苏南京 210019%中国人民解放军94701部队,安徽安庆 246000%中国人民解放军94347部队,辽宁沈阳 110042 |
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Author_FL | YANG Rennong YAN Mengda SHANG Jinxiang WANG Xin JI Huiming ZUO Jialiang |
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DocumentTitle_FL | Air combat target grouping based on improved CBSCAN algorithm |
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Keywords | 态势感知;目标分群;多编队协同空战;流形分布;改进CBSCAN算法 |
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Title | 改进cell密度聚类算法在空战目标分群中的应用 |
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