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.

这项工作涉及等步长自适应递归算法的分析和应用。PI应用Kushner首创的弱收敛理论，并结合预缩放来研究恒定步长算法的跟踪行为。然后将这些算法应用于谱线增强问题。在第二年，他计划推广自适应递归方法，并将其扩展到自适应格型滤波器，这已被发现在许多信号处理应用中非常有用。