Adaptive Signal Processing for Communications by Maximum Partial Likelihood Estimation

通过最大部分似然估计进行通信的自适应信号处理

• 批准号: 9614236
• 负责人: Tulay Adali
• 金额: $ 6万
• 依托单位: University of Maryland Baltimore County
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-09-01 至 1998-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9614236&HistoricalAwards=false
• 关键词: Adaptive; Signal; Processing; Communications; Maximum

This research aims at developing a unified statistical framework for nonlinear signal processing for communications by using a recent extension of maximum likelihood estimation, the maximum partial likelihood (MPL) estimation theory, which allows for dependent and missing observations, and sequential processing of data using only the information that is available at the time of processing. Previous research has established the theoretical foundation for the statistical analysis of MPL estimation with a general nonlinear probability model and provided a very useful information theoretic connection. The inclusion of the dependent data in the framework allows addressing of problems in coding and modulation in the presence of sources and channels with memory. This research includes development of a new class of realtime adaptive signal processing algorithms based on MPL estimation by using gradient optimization and information theoretic alternating projections, and the study of their statistical and dynamic properties and implementations in specific communications applications such as equalization in long distance optical fiber communications and voiced/unvoiced/transition classification in variable rate speech coding. Another important part of the effort is the investigation of the choices for the conditional probability model within the MPL framework, for which, along with other nonlinear models, such as polynomials, finite normal mixtures, and neural networks, piecewise linear models are used to provide a highly desirable compromise between the approximation ability of nonlinear structures and the efficiency and theoretical accessibility of the linear domain to develop new adaptive algorithms. The techniques which result from this work are very likely to lead to breakthroughs in realtime signal processing for communications via new algorithms with superior performances and in the general theory of nonlinear techniques in communications via rigorous treatment of t he underlying dependent time series problems.

这项研究旨在通过使用最大似然估计，最大偏差（MPL）估计理论的最新扩展，以开发一个用于通信的非线性信号处理的统一统计框架，该理论允许依赖和缺失的观察值以及仅使用在处理时可用的信息进行数据处理。 先前的研究已通过一般的非线性概率模型为MPL估计的统计分析建立了理论基础，并提供了非常有用的信息理论联系。 在框架中包含相关数据允许在有内存的来源和通道的情况下解决编码和调制中的问题。 这项研究包括通过使用梯度优化和信息理论交替的预测来开发基于MPL估计的新的实时自适应信号处理算法，以及对它们在特定通信应用中的统计和动态属性以及在诸如长距离光纤通信中的均衡中的统计和动态属性以及实现的研究。 努力的另一个重要部分是对MPL框架中条件概率模型的选择进行调查，其中以及其他非线性模型（例如多项式，有限的正常混合物和神经网络），分段线性模型可用于提供高度可取的非线性结构近似能力，并在非线性结构的近似能力之间造成良好的损害，以创建效率的近似能力，从而提供了效率的近似能力。算法。 这项工作所产生的技术很可能会导致通过具有出色性能的新算法以及通过对基本相关时间序列问题的严格处理在通信中的非线性技术的一般理论中进行通信的实时信号处理的突破。