对无人机目标定位的非线性错误补偿和智能优化方法的研究
Yinglei Li1, Qingping Hu1, Shiyan Sun1
1Graduate School, Naval University of Engineering, 717 Jiefang Road, Qiaokou District, Wuhan 430030, China.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
本研究引入了一种改进的子优化算法 (KYCOA),通过最小化非线性扰动和坐标转换的错误来提高无人机 (UAV) 目标定位精度.
科学领域:
- 航空航天工程 航空航天工程
- 机器人技术 机器人技术 机器人技术
- 优化算法 优化算法
背景情况:
- 无人机中的高精度目标定位受到非线性扰动和累积坐标转换错误的挑战.
- 多源错误合降低了无人机目标定位的定位精度.
研究的目的:
- 开发一种有效的错误分配方法,以提高无人机目标定位精度.
- 为解决无人机多源错误合导致的定位准确度下降的问题.
主要方法:
- 建立了一个多坐标系统转换模型来分析非线性错误传输.
- 使用泰勒扩展推导合成错误模型的线性错误转移.
- 提出了改进的子优化算法 (KYCOA),增强了种群多样性和位置更新机制.
主要成果:
- 与COA,GWO和WOA相比,KYCOA在模拟中显示了定位误差距的显著减少.
- 真实飞行测试显示,使用KYCOA.的目标点定位错误平均减少了40%以上.
- 提出的方法有效地提高了无人机中的目标定位精度和稳定性.
结论:
- KYCOA算法为优化无人机目标定位中的错误分配提供了一个强大的解决方案.
- 该方法通过减轻非线性扰动和坐标转换误差,显著提高定位准确性.
- KYCOA在现实世界无人机应用中被证明是有效的,改善了目标定位性能.
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