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一个集中式和一个非集中式复杂高斯随机变量的系数
1National Institute of Standards and Technology, Boulder, CO 80305, USA.
概括
本研究分析了两个独立的复杂随机变量的比率. 推导的概率密度函数在规范化过程中变得简单,并对大平均值进行近似的正常分布,有助于复杂的信号处理.
科学领域:
- 可能性理论概率理论.
- 复杂分析 复杂分析
- 信号处理 信号处理
背景情况:
- 调查两个独立的复杂随机变量的分数的统计性质.
- 解决数值为零的平均值,分母为非零的平均值的场景.
研究的目的:
- 为了得出分数的模量,相角,实数和虚数部分的统计数据.
- 在规范化后确定分数的概率密度函数 (PDF).
- 分析大平均值的分数的行为及其与实际应用的关系.
主要方法:
- 复杂的随机变量的规范化,以简化表述.
- 对模量和相角的统计数据的间接导出.
- 将统计结果扩展到真实和虚构部分.
- 对于大平均值的非对称分析.
主要成果:
- 分数的PDF表达为分母的平均值的函数.
- 分数接近一个正常分布的复杂随机变量大平均值.
- 来自剪切的随机变量的时刻,相关的信号处理.
- 呈现了复杂随机变量的比率的容忍区间.
结论:
- 由此衍生的统计属性为复杂的随机变量系数提供了全面的理解.
- 这些发现对复杂信号处理和微波计量有直接影响.
- 该研究为相关的统计信号处理领域的进一步研究提供了基础.
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