基于无人驾驶飞行器多源信息融合的异常容忍相对定位方法
He Song1, Yang Bi2,3, Shaolin Hu4
1School of Electronic Engineering, Xihang University, Xi'an, 710077, China.
Scientific reports
|May 10, 2025
概括
本研究引入了一种新的异常值容忍方法,用于无人机 (UAV) 的相对定位. 这种方法提高了空中加油和形成控制等任务的可靠性.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 航空航天工程 航空航天工程
- 数据融合和估计理论.
背景情况:
- 准确的相对定位对于无人机 (UAV) 操作至关重要,例如自主空中加油,集群形成和合作控制.
- 传统的基于最小平方 (LS) 的多源信息融合方法对异常值的耐受性较差,损害了定位计算的可靠性.
- 在复杂的无人机场景中,对强大的定位技术的需求至关重要,以确保任务成功和安全.
研究的目的:
- 开发一种改进的多源信息融合方法,用于无人机的相对定位.
- 增强现有的基于最小平方的方法的异常值容忍度.
- 在存在错误数据点的情况下,确保可靠的相对定位计算.
主要方法:
- 设计了一种新的损失函数,用零碎函数取代传统的抛物线函数.
- 通过利用这种新的损失函数来实现多源信息融合,建立了一个异常值容忍的相对定位方法.
- 拟议的方法通过模拟进行了评估,以评估其与传统方法相比的性能.
主要成果:
- 开发的方法证明了显著的异常值容忍能力.
- 对相对定位计算的异常值的不利影响得到了有效的缓解.
- 定位结果的可靠性得到了提高,而不会对准确性产生重大影响.
结论:
- 拟议的异常值容忍相对定位方法为无人机提供了强大的解决方案.
- 这种技术确保了可靠的数据融合和准确的定位,即使是异常数据.
- 它适用于需要高精度和可靠性的关键无人机应用.
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