基于蚁群优化算法的水下航路规划

张 毅; 高永琪; 牛兴江

doi:10.11993/j.issn.1673-1948.2013.04.008

基于蚁群优化算法的水下航路规划

doi: 10.11993/j.issn.1673-1948.2013.04.008

海军工程大学兵器工程系, 湖北武汉, 430033

详细信息

中图分类号: TJ630.33
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- 被引次数: 0
出版历程
- 收稿日期: 2013-03-29
- 修回日期: 2013-06-14
- 刊出日期: 2013-08-20

Underwater Route Planning Based on Ant Colony Optimization Algorithm

Department of Weaponry Engineering, Navy University of Engineering, Wuhan 430033, China

摘要

摘要: 为了确保水下地形匹配辅助导航系统在规划航路上获得充足的地形信息, 从而获得良好的匹配效果, 运用蚁群优化算法设计了航路优化搜索方法, 研究了基于水下地形匹配辅助导航的潜航器航路规划问题。定义了可行域的概念, 并详细讨论了算法中信息素的表示方法、启发式函数的设计以及信息素的更新规则, 运用算法得到了基于丰富地形信息的最优航路。最后在真实地形数据的基础上运用质点滤波(PMF)算法进行了地形匹配仿真研究, 仿真结果表明, 运用算法所规划的航路能够在水下地形匹配辅助导航中获得优良的地形匹配效果。
- 水下地形辅助导航 /
- 蚁群算法 /
- 航路规划 /
- 质点滤波
Abstract: To ensure the underwater terrain-aided navigation system can obtain sufficient terrain information on planned route, a route planning method is designed with the ant colony algorithm, and the underwater route planning for ter-rain-aided navigation is studied. The concept of feasible region is defined, and the pheromone representation, heuristic functions and pheromone updating rules are discussed in detail. Simulation with point-mass filter(PMF) is conducted, and the results show that the proposed route planning method can achieve good match in underwater terrain-aided navi-gation.
- underwater terrain-aided navigation /
- ant colony algorithm /
- route planning /
- point-mass filter

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参考文献(12)

[1]	张红梅, 赵建虎, 杨鲲, 等. 水下导航定位技术[M]. 武汉: 武汉大学出版社, 2010: 73-74.
[2]	郑彤, 蔡龙飞, 王志刚, 等. 地形匹配辅助导航中匹配区域的选择[J]. 中国惯性技术学报, 2009, 17(2): 191-196. Zheng Tong, Cai Long-fei, Wang Zhi-gang, et al. Selection of Matching Area in Terrain Match Aided Navigation[J]. Journal of China Inertial Technology, 2009, 17(2): 191-196.
[3]	谌剑, 李恒, 张静远. 水下地形辅助导航最优航路规划[J].鱼雷技术, 2012, 20(4): 276-380. Shen Jian, Li Heng, Zhang Jing-yuan. Optimal Path Planning Method for Underwater Terrain-aided Navigation[J]. Torpedo Technology, 2012, 20(4): 276-380.
[4]	Dorigo M, Thomas S. Ant Colony Optimization[M]. Brussels: MIT, 2004: 33-45.
[5]	马良, 朱刚, 宁爱兵. 蚁群优化算法[M]. 北京: 科学出版社, 2008: 15-47, 188-201.
[6]	Dorigo M, Gambardella L M. Ant Colony System: A Coop-erative Learning Approach to the Traveling Salesman Prob-lem[J]. IEEE Transactions on Evolutionary Computation (S1089- 778X), 1997, 1(1): 53-66.
[7]	林沂, 晏磊, 童庆禧. 针对水下辅助导航相关匹配算法的特征区最优航迹规划[J]. 吉林大学学报(工学版), 2008, 38(2): 439-443. Lin Yi, Yan Lei, Tong Qing-xi. Optimum Trajectory Plan- ning in Characteristic Areas for Underwater Aided Naviga-tion Correlation Matching Algorithms[J]. Journal of Jilin University(Engineering and Technology Edition), 2008, 38(2): 439-443.
[8]	周启鸣, 刘学军. 数字地形分析[M]. 北京: 科学出版社, 2006: 2-4.
[9]	高曼, 刘以安, 张强. 优化蚁群算法在反舰导弹航路规划中的应用[J]. 计算机应用, 2012, 32(9): 2530-2533. Gao Man, Liu Yi-an, Zhang Qiang. Application of Improved Ant Colony Algorithm to Route Planning of Anti-ship Miss- ile[J]. Journal of Computer Applications, 2012, 32(9): 2530- 2533.
[10]	李志林, 朱庆. 数字高程模型[M]. 武汉: 武汉大学出版社, 2001: 33-36.
[11]	Anonsen K, Hallingstad O. Terrain Aided Underwater Navi-gation Using Point Mass and Particle Filters[J]. Position, Location, And Navigation Symposium, 2006 IEEE/ION.
[12]	刘洪. 基于PMF算法的水下地形匹配技术研究[D]. 武汉: 海军工程大学, 2012.