基于改进的实证模式分解,研究用于无人机检测的声音源定位方法
Tao Chen1, Jiyan Yu1, Zhengpeng Yang1
1School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Sensors (Basel, Switzerland)
|May 11, 2024
概括
本研究提出了一种用于无人机的新型声学定位方法,使用改进的经验模式分解 (EMD) 和自适应频率窗口. 该技术可实现高精度的实时无人机检测和跟踪.
科学领域:
- 声学和信号处理
- 航空航天工程 航空航天工程
- 机器人和控制系统 机器人和控制系统
背景情况:
- 无人机的传统雷达和视觉追踪方法在某些操作环境中面临限制.
- 准确的本地化对于无人机交通管理,安全和运营安全至关重要.
- 开发替代本地化技术对于扩大无人机作战能力至关重要.
研究的目的:
- 为无人机提出和验证一种创新的声源定位方法.
- 提高无人机定位的准确性和可靠性,特别是当雷达或视觉方法不可行时.
- 为检测和定位小型无人机提供高效的实时解决方案.
主要方法:
- 应用平滑过和强大的实证模式分解 (REMD) 到无人机飞行信号.
- 利用由灰狼优化器 (GWO) 优化的自适应频率窗口,提取相关的内在模式功能 (IMF) 组件.
- 采用Chan-Taylor定位算法,加权最小平方,使用计算的传感器时间差异来定位目标.
主要成果:
- 拟议的声学定位方法在模拟和现实世界的测试中表现出强度和高性能.
- 在15米×15米的测量区域内,定位错误始终低于5%.
- 该方法在实时检测和确定小型无人机的位置方面被证明是有效的.
结论:
- 开发的声学定位技术为无人机检测提供了高效和准确的解决方案.
- 整合REMD,自适应频率窗口和先进的本地化算法可以提高本地化精度.
- 这种方法为无人机定位在传统方法失败的具有挑战性的环境中提供了可行的替代方案.
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