使用基于决策树的方法同时估计阿齐木斯和高度角
Anabel Reyes Carballeira1, Felipe A P de Figueiredo1, Jose Marcos C Brito1
1National Institute of Telecommunications INATEL, Av. João de Camargo, 510-Centro, Santa Rita do Sapucaí 37540-000, MG, Brazil.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
本研究引入了一个决策树 (DT) 模型,用于使用机器学习 (ML) 精确的到达方向 (DOA) 估计. 与传统方法相比,DT模型显著减少了预测错误和时间.
科学领域:
- 信号处理 信号处理
- 机器学习 机器学习
- 天线理论天线理论
背景情况:
- 对天线阵列系统来说,准确估计信号角度至关重要.
- 现有的方法,如多重信号分类 (MUSIC),在速度和准确性方面存在局限性.
- 机器学习为改进到达方向 (DOA) 预测提供了潜力.
研究的目的:
- 开发和评估一种基于机器学习的方法,用于预测传入信号的度和高度角度.
- 在不同的条件下评估拟议模型的概括能力.
- 将拟议模型的性能与最先进的MUSIC算法进行比较.
主要方法:
- 通过天线阵列接收的信号信息来训练一个决策树 (DT) 模型.
- 该DT模型的设计是为了同时估计度和高度的角度.
- 进行了模拟,以测试模型的稳定性和通用性.
主要成果:
- 基于DT的方法证明了强大的DOA估计,很好地概括到未见的条件.
- 与MUSIC.相比,拟议的DT模型实现了超过90%的预测误差减少.
- 使用基于DT的方法,预测时间减少了约50%.
结论:
- 决策树 (DTs) 为信号接收中DOA估计提供了具有竞争力和高效的替代方案.
- 提出的基于机器学习的方法在准确性和速度上明显优于MUSIC算法.
- 这项研究强调了ML在提高天线阵列系统性能方面的潜力.
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