通过噪声增强多重信号分类对一位量化观测的DOA估计
Yan Pan1, Li Zhang1, Liyan Xu2
1College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, China.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
这项研究引入了一种噪声增强量化器单元 (NBQU),用于使用一位量化数据进行改进的到达方向 (DOA) 估计. 与现有的一位技术相比,NBQU方法提高了准确性和分辨率.
科学领域:
- 信号处理 信号处理
- 阵列信号处理 阵列信号处理
- 信息理论 信息理论
背景情况:
- 一位量子化数据为到达方向 (DOA) 估计提供了低复杂度的实现.
- 现有的信号处理方法在一位数据下难以达到足够的估计准确性.
- 多重信号分类 (MUSIC) 是一个常见的DOA估计算法.
研究的目的:
- 提出一种高效的1位MUSIC方法用于DOA估计,使用一种新的噪声增强定量器单元 (NBQU).
- 用一位量化数据提高DOA估计的估计准确度和分辨率.
- 调查注入噪声对一位DOA估计性能的影响.
主要方法:
- 在一位量子的统一线性阵列 (ULA) 之前,将噪声组件注入到接收数据中.
- 基于噪声注入的数据,设计一个噪声增强定量器单元 (NBQU).
- 开发一个高效的一位MUSIC算法,利用NBQU进行DOA估计.
主要成果:
- 拟议的基于NBQU的MUSIC方法与现有的一位MUSIC方法相比,显示出更高的估计准确度和分辨率.
- 数字结果证实了通过注入噪音实现的性能提升.
- 注入噪声的最佳平方根平均值 (RMS) 允许拟议方法的准确性接近使用完整模拟数据的MUSIC的准确性.
结论:
- 基于NBQU的MUSIC方法显著提高了对一位量化数据的DOA估计准确度和分辨率.
- 注入噪声,当优化管理时,对于提高一位DOA估计的性能至关重要.
- 这种方法为精确的DOA估计在低复杂度的一位系统中提供了一个有希望的解决方案.
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