Tracking Analysis of Recursive Stochastic Algorithms

递归随机算法的跟踪分析

• 批准号: 8711984
• 负责人: Bhaskar Rao
• 金额: $ 12.73万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1988
• 资助国家: 美国
• 起止时间: 1988-01-15 至 1990-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8711984&HistoricalAwards=false
• 关键词: Tracking; Analysis; Recursive; Stochastic; Algorithms

This work involves the analysis and application of constant step- size adaptive recursive algorithms. The PI is applying the weak convergence theory pioneered by Kushner and incorporating prescaling to study the tracking behavior of constant step-size algorithms. These algorithms will then be applied to the problem of spectral line enhancement. This in turn has practical application to the problem of detecting harmonic signals in noise, which occurs in communications, signal processing, geophysics, military applications, etc. In the second year, he plans to generalize the adaptive recursive method and extend it to adaptive lattice filters which have been found to be quite useful in many signal processing applications.

这项工作涉及恒定级数自适应递归算法的分析和应用。 PI应用了库什纳（Kushner）开创的弱收敛理论，并将预先限制为研究恒定阶跃算法的跟踪行为。 然后，这些算法将应用于光谱线增强的问题。 这反过来又对检测噪声中的谐波信号的问题具有实际应用，该问题发生在通信，信号处理，地球物理学，军事应用等中。在第二年，他计划将自适应递归方法概括为自适应晶格过滤器，这些方法在许多信号处理应用中都非常有用。