强大的稀有贝叶斯二维到达方向估计与增益阶段错误
Xu Jin1, Xuhu Wang1,2, Yujun Hou1
1School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
这项研究引入了一种强大的稀疏贝叶斯方法,用于使用L形数组进行到达方向 (DOA) 估计,有效地处理增益相误差. 新方法提高了DOA准确性和事件信号的角度分辨率.
科学领域:
- 信号处理 信号处理
- 阵列信号处理 阵列信号处理
背景情况:
- 获取阶段错误会降低到达方向 (DOA) 估计性能.
- 准确的DOA估计在雷达和声纳等各种应用中至关重要.
研究的目的:
- 为L形传感器阵列提出一个强大的稀疏贝叶斯二维DOA估计方法.
- 为了减轻增强相位错误对DOA估计准确性的影响.
主要方法:
- 引入了一个辅助角度来将2D DOA转换为两个1D问题.
- 使用交叉相关性共变矩阵子矩阵构建了一个稀疏表示模型.
- 雇佣期望最大化和稀疏贝叶斯学习用于代参数估计.
主要成果:
- 该方法有效地估计了近视角和高度角.
- 在DOA估计中获得了更高的精度和角度分辨率.
- 在增强阶段错误的存在下表现出强大的性能.
结论:
- 建议的稀疏贝叶斯法为使用L形数组进行2D DOA估计提供了强大的解决方案.
- 它通过解决增强相位错误,显著提高了估计准确度和角度分辨率.
- 辅助角度转换简化了估计过程.
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