• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Evolutionary and Bio-inspired Computation: Theory and Applications II >Using a Multi-Objective Genetic Algorithm for Developing AerialSensor Team Search Strategies
【24h】

Using a Multi-Objective Genetic Algorithm for Developing AerialSensor Team Search Strategies

机译：使用多目标遗传算法制定空中传感器团队搜索策略

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Finding certain associated signals in the modern electromagnetic environment can prove a difficult task due to signal characteristics and associated platform tactics as well as the systems used to find these signals. One approach to finding such signal sets is to employ multiple small unmanned aerial systems (UASs) equipped with RF sensors in a team to search an area. The search environment may be partially known, but with a significant level of uncertainty as to the locations and emissions behavior of the individual signals and their associated platforms. The team is likely to benefit from a combination of using uncertain a priori information for planning and online search algorithms for dynamic tasking of the team. Two search algorithms are examined for effectiveness: Archimedean spirals, in which the UASs comprising the team do not respond to the environment, and artificial potential fields, in which they use environmental perception and interactions to dynamically guide the search. A multi-objective genetic algorithm (MOGA) is used to explore the desirable characteristics of search algorithms for this problem using two performance objectives. The results indicate that the MOGA can successfully use uncertain a priori information to set the parameters of the search algorithms. Also, we find that artificial potential fields may result in good performance, but that each of the fields has a different contribution that may be appropriate only in certain states.
机译：由于信号特性和相关的平台策略以及用于查找这些信号的系统，在现代电磁环境中查找某些相关信号可能会证明是一项艰巨的任务。查找此类信号集的一种方法是在团队中采用配备有RF传感器的多个小型无人机系统（UAS）来搜索区域。搜索环境可能是部分已知的，但在单个信号及其关联平台的位置和发射行为方面存在很大程度的不确定性。使用不确定的先验信息进行规划和使用在线搜索算法来动态分配团队的组合可能会给团队带来好处。检查了两种搜索算法的有效性：阿基米德螺旋式（其中团队成员组成的UAS对环境不响应）和人工势场，其中它们使用环境感知和交互作用来动态指导搜索。多目标遗传算法（MOGA）用于使用两个性能目标来探索针对该问题的搜索算法的理想特性。结果表明，MOGA可以成功地使用不确定的先验信息来设置搜索算法的参数。此外，我们发现，人工势场可能会产生良好的性能，但是每个场都有不同的贡献，可能仅在某些状态下才适用。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号