CAREER: Maximum Partial Likelihood Methods for Communications

职业：通信的最大部分似然法

• 批准号: 9703161
• 负责人: Tulay Adali
• 金额: $ 20万
• 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: 1997
• 资助国家: 美国
• 起止时间: 1997-08-01 至 2003-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9703161&HistoricalAwards=false
• 关键词: CAREER; Maximum; Partial; Likelihood; Methods

We propose to develop a statistical framework for nonlinear signal processing for communications by using a recent extension of maximum likelihood estimation, the maximum partial likelihood (MPL) theory. MPL allows for dependent and missing observations and sequential processing of data using only the information that is available at the time of processing. The inclusion of the dependent data in the framework allows development of techniques for joint detection and estimation in the presence of sources and channels with memory, for example to combine maximum likelihood sequence detection with adaptive equalization. The proposed research includes development of a new class of real-time adaptive signal processing algorithms based on MPL estimation by using both gradient optimization and information-theoretic alternating projections, study of their statistical and dynamic properties, incorporation of order/complexity determination into the scheme, and implementations in equalization, joint equalization and sequence estimation, and variable rate speech coding. The educatimn goals of the PI are shaped by the commitment to equip the students with the necessary theoretical and practical facilities to solve real-world problems; to help them mature into resourceful and creative engineers who could meet the challenges of a discipline rapidly growing in complexity. Information on projects in this area and on maximum partial likelihood research can be found at http://engr.umbc.edu/~adali.

我们建议通过使用最大似然估计的最新扩展，即最大部分似然（MPL）理论，开发用于通信的非线性信号处理的统计框架。 MPL 允许仅使用处理时可用的信息进行相关观察和缺失观察以及数据的顺序处理。将相关数据包含在框架中允许开发在具有存储器的源和通道存在的情况下联合检测和估计的技术，例如将最大似然序列检测与自适应均衡相结合。所提出的研究包括开发一类基于 MPL 估计的新型实时自适应信号处理算法，使用梯度优化和信息论交替投影，研究其统计和动态特性，将阶数/复杂性确定纳入方案中，以及均衡、联合均衡和序列估计以及可变速率语音编码的实现。 PI 的教育目标是致力于为学生提供解决现实问题所需的理论和实践设施；帮助他们成长为足智多谋、富有创造力的工程师，能够应对快速复杂性增长的学科的挑战。有关该领域项目和最大部分似然研究的信息，请访问 http://engr.umbc.edu/~adali。