基于Hough变换和聚类的舰艇编队队形识别算法

doi:10.3969/j.issn.1000-1093.2016.04.011

兵工学报 ›› 2016, Vol. 37 ›› Issue (4): 648-655.doi: 10.3969/j.issn.1000-1093.2016.04.011

基于Hough变换和聚类的舰艇编队队形识别算法

张翼飞，董受全，毕开波

(海军大连舰艇学院导弹系，辽宁大连 116018)

收稿日期:2015-08-06 修回日期:2015-08-06 上线日期:2016-06-20
作者简介:张翼飞(1976—)男讲师博士后
基金资助:
海军装备部军内科研项目（2012年）

Warship Formation Recognition Algorithm Based on Hough Transform and Clustering

ZHANG Yi-fei， DONG Shou-quan， BI Kai-bo

(Deptment of Missile, Dalian Naval Academy, Dalian 116018, Liaoning, China)

Received:2015-08-06 Revised:2015-08-06 Online:2016-06-20

摘要/Abstract

摘要： 编队队形识别技术是反舰导弹武器系统目标识别领域中的一项重要研究内容，具有队形识别能力的反舰导弹可以有效增强对密集型舰艇编队当中重要目标的选择能力，进而直接提升导弹的命中概率和作战效能。基于Hough变换技术研究了一种舰艇编队队形识别算法，在无探测噪声影响时具有很好的识别率。当目标信息受污染较严重时，进一步采用了改进的K均值聚类算法对Hough变换后得到的积累矩阵局部峰值进行聚类处理，根据峰值聚类的结果准确提取出待识别队形的参数，从而有效抑制了探测噪声带来的不利影响。仿真结果表明，采用该算法可以正确识别出舰艇编队队形，在目标信息受污染较严重时也具有较好的识别效果，具有较好的鲁棒性。对该算法复杂度及目标指示误差对算法精度的影响进行了分析。

关键词: 飞行器控制、导航技术, 队形识别, Hough变换, 改进K均值聚类, 峰值聚类

Abstract: Formation recognition is an important research task in the area of target recognition for anti-ship missile weapon systems. Perfect formation recognition capability can improve the target selection of anti-ship missiles for compact warship formation, thus enhancing the hit probability and operational effectiveness of anti-ship missiles. The formation recognition algorithm is researched base on Hough transform, which has higher recognition rate without the influence of detection noise. If the target information is polluted badly, the improved K-means clustering algorithm is used to cluster the local peaks in an accumulation matrix. The shape parameters of formation to be recognized can be extracted from the clustering results so that the adverse influence due to detection noise is restrained effectively. Even though the target information is polluted badly, the algorithm has better recognition accuracy and robustness. The complexity of the algorithm and the effect of target designation error on the accuracy of the algorithm are analyzed. The simulation results show that the proposed algorithm has the perfect capability of formation recognition.

Key words: control and navigation technology of aerocraft, formation recognition, Hough transform, improved K-means clustering, peak clustering

中图分类号:

TP391

张翼飞，董受全，毕开波. 基于Hough变换和聚类的舰艇编队队形识别算法[J]. 兵工学报, 2016, 37(4): 648-655.

ZHANG Yi-fei， DONG Shou-quan， BI Kai-bo. Warship Formation Recognition Algorithm Based on Hough Transform and Clustering[J]. Acta Armamentarii, 2016, 37(4): 648-655.

参考文献

［1］沈建锋. 舰艇队形识别目标选择算法[J]. 火力与指挥控制, 2010, 35(9): 98-100.
SHEN Jian-feng. A target selection algorithm using naval ships formation identification[J]. Fire Control & Command Control, 2010, 35(9): 98-100. (in Chinese)
[2］乔冰, 肖滨. 基于模板匹配的战斗舰艇队形自动识别研究[J]. 计算机仿真, 2006, 23(9): 4-6.
QIAO Bing, XIAO Bin. Identification of combat warship formation based on template matching[J]. Computer Simulation, 2006, 23(9): 4-6. (in Chinese)
[3］蔡益朝, 张维明, 贺玲,等. 一种基于Hough变换的线型群体队形识别方法[J]. 国防科技大学学报, 2006, 28(2): 124-130.
CAI Yi-chao, ZHANG Wei-ming, HE Ling, et al. Formation recognition based on Hough transform[J]. Journal of National University of Defense Technology, 2006, 28(2): 124-130. (in Chinese)
[4] Paulino A A, Feng J J, Jain A K. Latent fingerprint matching using descriptor-based Hough transform[J]. IEEE Transactions on Information Forensics and Security, 2013, 8(1): 31-45.
[5] Rodriguez L A F, Uribe J A, Bonilla J F V. Obstacle detection over rails using Hough transform[C]∥2012 XVII Symposium of Image, Signal Processing, and Artificial Vision(STSIVA). Antioquia: IEEE, 2012: 317-322.
[6] HOUGH P V C．A method and means of recognizing complex patterns: US, 3069645 [P]. 1962-12-18.
[7] Ebrahimpour R, Rasoolinezhad R, Hajiabolhasani Z, et al. Vanishing point detection in corridors: using Hough transform and K-means clustering[J]. IET Computer Vision , 2012, 6(1): 40-51.
[8］ Zhou T H, Papanikolopoulos N. Enhancing the randomized Hough transform with k-means clustering to detect mutually-occluded ellipses[C]∥Proceedings of 19th Mediterranean Conference on Control & Automation. Corfu: IEEE, 2011: 1358-1363.
[9］ Yu S, Tranchevent L C, Liu X H, et al. Optimized data fusion for kernel K-means clustering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(5): 1031-1039.
[10］钮建伟, 李志忠. 基于形状的三维头面型聚类分析[J]. 兵工学报, 2009, 30(8): 1084-1088.
NIU Jian-wei, LI Zhi-zhong. Shape-based 3D head data clustering[J]. Acta Armamentarii, 2009, 30(8): 1084-1088.(in Chinese)
[11］杨善林，李永森，胡笑旋,等. K-means算法中的k值优化问题研究[J]. 系统工程理论与实践，2006, 26(2)：97-101.
YANG Shan-lin, LI Yong-sen, HU Xiao-xuan, et al. Optimization study on k value of K-means algorithm[J]. Systems Engineering-Theory & Practice, 2006, 26(2)：97-101. (in Chinese)
[12］祁亚芳, 杨明. 一种改进的随机Hough变换检测圆的方法[J]. 数学的实践与认识, 2014, 44(17): 189-195.
QI Ya-fang, YANG Ming. An improved method of the randomized Hough transforms to detect the circle[J].Mathematics in Practice and Theory, 2014, 44(17): 189-195. (in Chinese)

[1]	王学敏, 于洪波, 张翔宇, 安舒, 李文海. 基于Hough变换检测前跟踪的水下多目标被动检测方法[J]. 兵工学报, 2023, 44(7): 2114-2121.
[2]	黄隽, 吴鹏飞, 李晓宝, 刘玥. 基于Hough变换和优化K均值聚类的反舰导弹编队识别目标选择方法[J]. 兵工学报, 2023, 44(2): 472-483.
[3]	覃若琳，蒋晓刚，金良安，高可心. 基于Hough变换的水中气泡群特征参数提取方法研究[J]. 兵工学报, 2019, 40(12): 2504-2512.
[4]	张民，陈亮，陈欣. 基于简便约束粒子群优化算法的空空导弹μ-PID控制器设计[J]. 兵工学报, 2017, 38(1): 89-96.
[5]	胡东方，姬源浩. 基于灰关联决策的模块化光电吊舱可靠性分析[J]. 兵工学报, 2016, 37(8): 1506-1516.
[6]	张松, 侯明善. 飞行器轨迹优化的造型降维方法[J]. 兵工学报, 2016, 37(6): 1125-1130.
[7]	李强，卢宝刚，王晓辉，王永海，庄凌. 一种导弹终端侵彻多约束最优制导方法[J]. 兵工学报, 2016, 37(6): 1131-1137.
[8]	王红辉，杨绍卿，吴成富，郝峰，车晓涛. 旋转矢量在高动态全姿态飞行器运动方程中的应用[J]. 兵工学报, 2016, 37(3): 439-446.
[9]	张尧，郭杰，唐胜景，商巍，张浩强. 机动目标拦截含攻击角约束的新型滑模制导律[J]. 兵工学报, 2015, 36(8): 1443-1457.
[10]	李成，李建勋，童中翔，贾林通，张志波. 红外成像制导末端局部图像识别跟踪研究[J]. 兵工学报, 2015, 36(7): 1213-1221.
[11]	赵江，周锐. 基于倾侧角反馈控制的预测校正再入制导方法[J]. 兵工学报, 2015, 36(5): 823-830.
[12]	范世鹏，林德福，姚怀瑾，路宇龙，祁载康. 基于最优二次型理论的滚转导弹三回路驾驶仪设计与研究[J]. 兵工学报, 2015, 36(5): 831-838.
[13]	鲁天宇，尹健，杜肖，夏群利. 虚位技术对相控阵雷达导引头制导性能的影响[J]. 兵工学报, 2015, 36(12): 2262-2268.
[14]	于洪波，王国宏，张仲凯. 基于抛物线随机Hough变换的机载脉冲多普勒雷达机动弱目标检测前跟踪方法[J]. 兵工学报, 2015, 36(10): 1924-1932.
[15]	唐传林，黄长强，杜海文，黄汉桥，丁达理，罗畅. 无人作战飞行器编队协同攻击轨迹规划研究[J]. 兵工学报, 2014, 35(4): 523-530.

基于Hough变换和聚类的舰艇编队队形识别算法

Warship Formation Recognition Algorithm Based on Hough Transform and Clustering

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价