一个算法来估计来自艾伦偏差的功率光谱密度
概括
本研究引入了一种新的方法,用于将艾伦变量 (AVAR) 噪声配置文件转换为功率光谱密度 (PSD),以更好地进行系统模拟. 这使得关键电子系统中复杂噪声的准确建模成为可能.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 物理 物理学 物理
背景情况:
- 复杂的电子系统依赖于稳定的振荡器,用于通信和导航等功能.
- 振荡器稳定性通常用时间域中的艾伦变量 (AVAR) 来描述.
- 在频域中的功率光谱密度 (PSD) 提供了更完整的噪声表征,但从AVAR转换为PSD具有挑战性.
研究的目的:
- 开发一种用于将AVAR/HVAR配置文件转换为近似PSD的分析方法.
- 为了能够准确地模拟各种噪音类型和组合的复杂噪音.
- 为了使用NASA的深空原子钟数据验证该方法.
主要方法:
- 开发了一个分析算法,从时间域的AVAR/HVAR权力规律描述中近似PSD.
- 该方法允许从AVAR/HVAR转换为PSD,与以前的方法不同.
- 算法的自我验证是通过从生成的PSD重建AVAR/HVAR来实现的.
主要成果:
- 该研究提出了一种简单的方法,可以从AVAR/HVAR数据中生成PSD.
- 该方法成功地产生了端到端模拟的多色噪声,并与深空原子钟数据验证.
- 还报告了算法的连续版本的限制和分析表达式.
结论:
- 开发的方法提供了一种可靠的方式,将时间域振荡器稳定性测量 (AVAR/HVAR) 转化为频域描述 (PSD).
- 这有助于在复杂的电子系统中进行更准确,更全面的噪声建模,这对于性能优化至关重要.
- 该方法具有广泛的适用性,增强了从无线通信到太空导航等应用程序的模拟.
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