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.

我们建议开发一个统计框架的非线性信号处理的通信，通过使用最大似然估计，最大偏似然（MPL）理论的最新扩展。 MPL允许相关和缺失观测，以及仅使用处理时可用的信息对数据进行顺序处理。在框架中包含相关数据允许开发在存在具有记忆的源和信道的情况下进行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，联合收割机。建议的研究包括一类新的实时自适应信号处理算法的MPL估计的基础上，通过使用梯度优化和信息论交替投影，研究其统计和动态特性，将顺序/复杂度的确定纳入计划，并在均衡，联合均衡和序列估计，和可变速率语音编码的实施。 PI的教育目标是通过承诺为学生提供必要的理论和实践设施来解决现实世界的问题;帮助他们成熟为足智多谋和创造性的工程师，他们可以满足一个学科的挑战迅速增长的复杂性。关于这一领域的项目和最大偏似然研究的信息可在http://engr.umbc.edu/~adali上找到。