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.

这项研究涉及对多个传感器从多个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均衡和独立源（信号）的卷积混合的盲分离。 数字通信系统的多输入多输出（MIMO）模型出现在各种通信应用中：高速数字用户线路、多磁道数字磁记录、多用户/多址通信系统、多样性数字无线电、双极化无线电信道、多传感器声纳/雷达系统等。MIMO 信道建模允许采用统一且最优的方法来设计 MIMO 均衡器/滤波器/组合器，以抑制码间干扰 (ISI)、同信道和相邻信道干扰 (CCI)和 ACI）和多址干扰（MAI）。该领域的最新技术需要完全了解 MIMO 传递函数，这对于实际通信系统来说是不现实的。 在 MIMO 系统中，训练序列还必须由干扰产生源提供：这是一个完全不切实际的假设。该研究计划的目标之一是通过消除自适应多通道均衡器设计中对训练序列的需要，为上述具有重要实际意义的问题提供更实用的答案。基于二阶统计的方法和基于高阶统计的方法都在研究中，重点是前者。重点是与现有文献相比需要尽可能少假设的方法，例如子通道之间允许有公共零点，通道矩阵脉冲响应可以无限长等。所提出的盲源分离研究结果预计对从事广泛的多传感器数据处理和分析的科学家和工程师有用。声纳、雷达、声学阵列应用以及发电厂和土木工程监测等应用类别。盲均衡方面的工作预计将为高速数字用户线、多磁道数字磁记录和多用户无线通信等广泛的数字通信系统中的信号处理带来有效且计算效率高的算法。