用最大概率方法估计缩复杂正弦信号的缩因子估计
A Karthikeyan1, Amit Kumar Rahul2, Ravi Tiwari3
1Department of Mathematics, School of Advanced Sciences (SAS), Vellore Institute of Technology (VIT), Chennai, Tamil Nadu, India.
Scientific reports
|March 12, 2026
概括
本研究引入了一种高效的近似最大概率 (ML) 估计器,用于衰变信号中的减噪因子. 该方法实现了最佳性能和降低了复杂性,适合实时应用.
科学领域:
- 信号处理 信号处理
- 统计估计的统计估计.
背景情况:
- 精确估计减噪因子对于分析衰变信号至关重要.
- 对于某些应用,现有的方法可能缺乏效率或计算可行性.
研究的目的:
- 开发一个大致的最大概率 (ML) 估计框架,用于复杂的正弦信号的阻尼因子.
- 导出封闭式估计器并评估其统计效率和性能.
主要方法:
- 对缓因子的封闭形式近似ML估计器的导出.
- 使用克莱默-拉奥下限 (CRLB) 评估估计器效率.
- 在不同的信号噪声比 (SNR),样本大小和信号参数中进行性能分析.
主要成果:
- 拟议的近似ML估计器在小阻尼条件下实现了CRLB,证明了统计学最佳性.
- 估计准确度随着SNR的提高和样本大小的增加而增加.
- 经验差异趋于CRLB,验证了估计器的效率.
结论:
- 该框架提供了最佳估计,减少了计算复杂性,非常适合快速缩参数提取.
- 实际指导方针表明,适度的资源足以用于弱减噪信号.
- 应用包括结构健康监测,雷达和振动分析.
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