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优化无人机到达位置错误的角度,通过改进的破子优化算法.

Yinglei Li1, Qingping Hu2, Shiyan Sun1

  • 1College of Weaponry Engineering, Naval University of Engineering, 717 Jiefang Road, Qiaokou District, Wuhan, 430030, China.

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概括

这项研究引入了对无人机到达角度 (AOA) 定位的改进的Starling优化算法,大大减少了错误. 该方法通过解决非线性错误积累,提高无人机的定位精度.

关键词:
在空中运载的光电子.抵达的角度到达的角度.错误分析 错误分析目标被动本地化目标被动本地化

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科学领域:

  • 航空航天工程 航空航天工程
  • 信号处理 信号处理
  • 优化算法 优化算法

背景情况:

  • 传统的无人机到达角度 (AOA) 定位受到非线性错误积累的影响,降低了准确性.
  • 现有的优化算法经常陷入局部最佳状态,妨碍精确的定位.
  • 无人机的被动定位需要强大的方法来克服这些局限性.

研究的目的:

  • 为无人机开发一种新的定位错误优化方法.
  • 通过减轻非线性错误积累,提高AOA定位的准确性.
  • 通过防止局部最佳趋同来改进现有的优化算法.

主要方法:

  • 构建了一个多源错误传播模型来分析无人机位置,态度和态度错误.
  • 设计了一个分阶段优化框架,使用观察序列来抑制非线性错误积累.
  • 集成了一个改进的Starling优化算法与立方混乱映射和螺旋搜索策略错误源补偿.

主要成果:

  • 与传统的AOA定位相比,改进的算法实现了定位错误距离减少73.29%.
  • 在模拟中显示了58.12%的改进,与原来的Starling优化算法相比.
  • 在纠正非线性扰动方面明显优于其他比较算法.

结论:

  • 提出的方法有效地纠正无人机电光系统中的非线性扰动.
  • 这种方法为无人机的被动定位提供了更高精度的解决方案.
  • 整合阶段性校正和改进的星灵优化提供了一个强大的定位策略.