Spatio-Temporal Statistical Signal Processing For Blind Equalization and Source Separation

用于盲均衡和源分离的时空统计信号处理

• 批准号: 9803850
• 负责人: Jitendra Tugnait
• 金额: $ 6.29万
• 依托单位: Auburn University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-09-01 至 2002-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9803850&HistoricalAwards=false
• 关键词: Spatio; Temporal; Statistical; Signal; Processing

This research is concerned with analysis and processing of stochastic signals received at multiple sensors from multiple sources with focus on blind equalization of digital communications signals and on blind separation of convolutive mixtures of independent sources (signals). Multiple-input multiple-output (MIMO) models of digital communication systems arise in a wide variety of communications applications: high-speed digital subscriber lines, multi-track digital magnetic recording, multiuser/multi-access communications systems, digital radio with diversity, dually polarized radio channels, multisensor sonar/radar systems, etc. MIMO channel modeling allows for a unified and optimal approach to design of MIMO equalizers/filters/combiners for suppression of intersymbol interference (ISI), cochannel and adjacent channel interferences (CCI and ACI) and multi-access interferences (MAI). State-of-the-art in this area requires complete knowledge of the MIMO transfer function which is unrealistic for practical communication systems. In MIMO systems the training sequences must also be provided by the interference-generating sources: an utterly unrealistic assumption. One of the goals of this research program is to provide more practical answers to the above problems of great practical importance by removing the need for training sequences for adaptive multichannel equalizer design. Both second-order statistics-based and higher-order statistics-based approaches are being investigated with emphasis on the former. Emphasis is on approaches that require as few assumptions as possible compared to existing literature, e.g. common zeros among the subchannels are allowed, the channel matrix impulse response can be infinitely long, etc. The results of the proposed research on blind source separation are expected to be useful to scientists and engineers engaged in processing and analysis of multisensor data in a broad class of applications such as sonar, radar, acoustic array ap plications and monitoring of power plants and civil works. The work on blind equalization is expected to result in effective and computationally efficient algorithms for signal processing in a broad class of digital communication systems such as high-speed digital subscriber lines, multi-track digital magnetic recording and multiuser wireless communications.

这项研究涉及从多个来源接收到的随机信号的分析和处理，重点是数字通信信号的盲目均衡以及独立来源（信号）的复杂混合混合物的盲目分离。 数字通信系统的多输入多输出（MIMO）模型在各种通信应用中都出现：高速数字订户线路，多轨数字磁性记录，多端/多端/多ACCESS通信系统，具有多样性的数字无线电，多样化的无线电频道，多偏光无线电通道，多层传感器/雷达/雷达/雷达/雷达系统等。抑制隔膜间干扰（ISI），门口和邻近通道干扰（CCI和ACI）以及多ACCESS干扰（MAI）。在这一领域的最先进需要完全了解MIMO转移功能，这对于实际通信系统是不现实的。 在MIMO系统中，训练序列还必须由干扰生成的来源提供：一个完全不现实的假设。该研究计划的目标之一是通过消除对自适应多通道均衡器设计的训练序列的需求，为上述非常重要的问题提供更实际的答案。正在研究基于二阶统计和基于高阶统计的方法，重点是前者。重点是与现有文献相比需要尽可能少的假设的方法，例如允许在亚渠道中进行常见的零，通道矩阵脉冲响应可以无限长，等等。拟议的盲目分离研究的结果预计将对从事多种传感器数据进行处理和分析的科学家和工程师有用，例如Sonar，Radar，Radar，Ocoustic Argy Ap Plications和Power Locter and Power Locter and Power Locter and Pottonare and Monitoring and Pottoner and Monitors和Commorting and Pottoner和Commort of Power and Corress and Nibly and Corress。预计盲目均衡的工作将导致在广泛的数字通信系统（例如高速数字订户线路，多轨数字磁性记录和多源无线通信）中为信号处理的有效和计算有效算法。