在FANET中使用斐波纳契采样进行DOA估计的统一道数组
Siwei Huo1, Ming Zhang1, Yongxi Liu1
1School of Information and Communications Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Sensors (Basel, Switzerland)
|May 14, 2025
概括
本研究引入了一种改进的到达方向 (DOA) 估计方法,用于无人飞行器 (UAV) 网络. 新的统一道阵列 (UFA) 和斐波那契采样显著提高了定位准确度,在卫星导航失败的地方.
科学领域:
- 无线通信无线通信
- 信号处理 信号处理
- 导航系统 导航系统
背景情况:
- 飞行特设网络 (FANET) 对6G通信系统至关重要.
- 无人驾驶飞行器 (UAV) 的精确定位至关重要,特别是当卫星导航不可用时.
- 现有的到达方向 (DOA) 估计方法在FANET中对无人机缺乏准确性,特别是在较大的极角.
研究的目的:
- 为FANET中的无人机提出一个简单而准确的DOA估计方法.
- 在被拒绝卫星导航信号的环境中提高定位准确性.
- 在采样策略中解决极地聚类现象.
主要方法:
- 使用统一道阵列 (UFA) 配置的改进的相关干扰仪方法.
- UFA 结合了一种统一的圆形阵列 (UCA) 和一个额外的中央元件,用于垂直光圈的利用.
- 斐波纳契抽样策略以减轻极点聚类,部分相差的使用,以及三角函数用于相似性计算.
主要成果:
- 拟议的UFA配置提高了65.56%的DOA估计准确度,与平面UCA大极角相比.
- 斐波纳契采样比传统的度-经度采样提高了11.54%的DOA估计准确度.
- 通过方法优化实现了减少存储负担和提高计算效率.
结论:
- 开发的基于UFA的相关干扰仪方法为UAV在FANET中提供了卓越的DOA估计准确性.
- 该方法有效地解决了由大极角和采样限制所带来的挑战.
- 这一进步有助于在6G通信系统中实现强大而精确的无人机导航.
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