CIF:Small: Recursive Estimation of Randomly Modulated Processes

CIF:Small：随机调制过程的递归估计

• 批准号: 0916568
• 负责人: Yariv Ephraim
• 金额: $ 47.32万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2013-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916568&HistoricalAwards=false
• 关键词: CIF; Small; Recursive; Estimation; Randomly

Project Abstract: CCF 0916568?CIF: Small: Recursive Estimation of Randomly Modulated Processes?Many phenomena observed in science and engineering exhibit random variations over time. Internet traffic, speech signals, and biological signals, are a few such examples. Choosing a random process to model such phenomena involves a tradeoff between accuracy versus complexity. This project investigates fast computational methods for modeling random phenomena using a class of versatile random processes called randomly modulated processes. A randomly modulated process consists of two simpler random processes, one which is observable and another which modulates the observable process. By exploiting the structural properties of randomly modulated processes, the investigators are devising efficient and accurate methods to model a wide class of random phenomena. Specifically, the project develops recursive estimators to characterize Internet traffic accurately in real-time. Other important applications of the estimators can be found in speech processing, nuclear medicine, biology, genetics, and finance.The project focuses on the intertwined problems of signal and parameter estimation of randomly modulated processes. Using a transformation of measure approach, the investigators are developing recursive estimators for such processes and investigating feasible approaches for solving the associated stochastic differential equations. The research involves in-depth investigation and comparison of the transformation of measure approach with respect to traditional likelihood-based approaches that lead invariably to batch algorithms that can only be executed offline. The project also involves the derivation of asymptotic properties of maximum likelihood estimators for randomly modulated processes. Implementations of the recursive estimators are being applied to Internet traffic traces, with the objective of leveraging the estimators for network admission control and anomaly detection.

项目摘要：CCF 0916568？CIF：小：随机调制过程的递归估计？在科学和工程中观察到的许多现象随着时间的推移而显示出随机变化。 互联网流量，语音信号和生物信号就是一些这样的例子。 选择一个随机过程来建模这种现象涉及准确性与复杂性之间的权衡。该项目研究了使用一类称为随机调制过程的多功能随机过程对随机现象进行建模的快速计算方法。 一个随机调制过程由两个更简单的随机过程组成，一个过程可观察到一个可观察到的可观察过程。 通过利用随机调制过程的结构特性，研究人员正在设计有效而准确的方法来对广泛的随机现象进行建模。具体而言，该项目开发了递归估计器，以实时准确表征互联网流量。 估计器的其他重要应用可以在语音处理，核医学，生物学，遗传学和金融中找到。该项目着重于信号的交织问题和随机调制过程的参数估计。 使用测量方法的转换，研究人员正在为此类过程开发递归估计器，并研究了解决相关随机微分方程的可行方法。 该研究涉及对基于传统可能性的方法的量度方法的深入研究和比较，这些方法总是导致只能离线执行的批处理算法。 该项目还涉及随机调制过程的最大似然估计器的渐近性能。 递归估计器的实现正在应用于互联网交通轨迹，目的是利用估计器进行网络入院控制和异常检测。