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基于CRLB被动目标定位标准的可变速度无人机路径优化.

Lijia Chen1, Chengfeng You1, Yixin Wang1

  • 1School of Aeronautics and Astronautics, Sichuan University, Chengdu 610207, China.

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PubMed
概括
此摘要是机器生成的。

优化无人飞行器 (UAV) 群路径可以提高被动目标定位的准确性. 在环状区域的可变速度无人机提高了性能,通过粒子群优化 (PSO) 验证了这一点.

关键词:
克拉默拉奥的下界 (CRLB)粒子群算法 (PSO) 是一种路径优化方法的路径优化方法.目标被动本地化目标被动本地化无人驾驶飞行器 (UAV) 是一种无人驾驶飞行器.变速变量的速度变量

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科学领域:

  • 机器人和控制系统 机器人和控制系统
  • 信号处理 信号处理
  • 优化算法 优化算法

背景情况:

  • 被动目标定位的准确性在很大程度上取决于无人机 (UAV) 群组配置.
  • 现有的方法通常假定UAV的速度是固定的,这限制了可行的定位策略.

研究的目的:

  • 调查无人机路径优化,以提高被动目标定位精度.
  • 在路径规划中应对可变无人机速度的挑战.

主要方法:

  • 开发了一种使用到达时间差 (TDOA) 的被动目标定位模型,并通过Chan方法和泰勒系列扩展进行了增强.
  • 对于指导无人机定位的修改的TDOA算法,衍生出克拉默-拉奥下限 (CRLB).
  • 包含可变UAV速度,将可行的区域扩展到环状的形状.
  • 应用粒子群集优化 (PSO) 进行有效的路径优化.

主要成果:

  • 拟议的方法通过优化环形可行区域内的无人机位置来提高本地化准确性.
  • 考虑可变速度可以扩大操作范围并提高本地化性能.
  • PSO算法有效地解决了路径优化的计算复杂性增加.

结论:

  • 无人机路径优化显著提高了被动目标定位精度.
  • 考虑到可变速度和环状区域的新方法提供了卓越的性能.
  • 在本地化任务中,PSO提供了一种有效的解决方案,用于实时无人机路径规划.