| 引用本文: | 闫孟达,杨任农,王新,等.改进cell密度聚类算法在空战目标分群中的应用.[J].国防科技大学学报,2021,43(4):108-117.[点击复制] |
| YAN Mengda,YANG Rennong,WANG Xin,et al.Air combat target grouping based on improved CBSCAN algorithm[J].Journal of National University of Defense Technology,2021,43(4):108-117[点击复制] |
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| 改进cell密度聚类算法在空战目标分群中的应用 |
| 闫孟达1,杨任农1,王新2,左家亮1,嵇慧明3,尚金祥4 |
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(1. 空军工程大学 空管领航学院, 陕西 西安 710051;2. 中国人民解放军94994 部队, 江苏 南京 210019;3. 中国人民解放军94701 部队, 安徽 安庆 246000;4. 中国人民解放军94347 部队, 辽宁 沈阳 110042)
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| 摘要: |
| 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。 |
| 关键词: 态势感知 目标分群 多编队协同空战 流形分布 改进CBSCAN算法 |
| DOI：10.11887/j.cn.202104014 |
| 投稿日期：2020-01-16 |
| 基金项目:国家自然科学基金资助项目(61503409)；国家社会科学基金资助项目(2019-SKJJ-C-026) |
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| Air combat target grouping based on improved CBSCAN algorithm |
| YAN Mengda1, YANG Rennong1, WANG Xin2, ZUO Jialiang1, JI Huiming3, SHANG Jinxiang4 |
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(1.Air Traffic and Navigation College, Air Force Engineering University, Xi′an 710051 China;2. The PLA Unit 94994 Nanjing 210019, China;3. The PLA Unit 94701 Anqing 246000, China;4. The PLA Unit 94347 Shenyang 110042, China)
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| Abstract: |
| Aiming at the shortcomings of the traditional clustering algorithm on the clustering effect of manifold data, and the low real-time performance, the improved CBSCAN (cell-based density spatial clustering of applications with noise) was proposed to solve air combat target grouping issue. By analyzing the air combat situation parameters, the general model of air combat target grouping was established and the target grouping was transformed into clustering problem. Then, the target grouping model based on improved CBSCAN was established by improving the clustering method of CBSCAN algorithm. Through simulation experiments, the clustering accuracy and real-time performance of K-means, expectation maximum algorithm, density peak algorithm, density-based spatial clustering of applications with noise algorithm, CBSCAN algorithm and improved CBSCAN algorithm in 30 combat situations were compared and analyzed. The results show that the improved CBSCAN algorithm can correctly group multi-target formations under the condition of unknown number of formations and target manifold distribution, and the real-time performance was improved by about 30% compared with the original algorithm, which shows the practical application value of the proposed method. |
| Keywords: situational awareness target grouping multi-team cooperative air combat manifold distribution improved CBSCAN algorithm |
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