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.

本研究关注的是分析和处理的随机信号接收在多个传感器从多个来源，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）模型出现在各种各样的通信应用中：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、具有分集的数字无线电、双极化无线电信道、多传感器声纳/雷达系统MIMO信道建模允许统一且最佳的方法来设计用于抑制符号间干扰（ISI）的MIMO均衡器/滤波器/组合器，同信道和相邻信道干扰（CCI和ACI）以及多址干扰（MAI）。该领域的最新技术要求完整地了解MIMO传递函数，这对于实际通信系统是不现实的。 在MIMO系统中，训练序列也必须由干扰产生源提供：这是一个完全不切实际的假设。本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要，为上述具有重要实际意义的问题提供更实际的答案。二阶和高阶的基于代数的方法正在研究与重点放在前者。重点是与现有文献相比需要尽可能少的假设的方法，例如，允许子信道之间的公共零点，信道矩阵冲激响应可以无限长，盲源分离的研究结果对从事多传感器数据处理和分析的科学家和工程师有着广泛的应用前景例如声纳、雷达、声学阵列应用以及发电厂和土木工程的监测。盲均衡的工作有望为诸如高速数字用户线、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产生有效的和计算效率高的算法。