CAREER: A Research, Education, and Technology Transfer Proposal in Statistical Signal Processing with Emphasis on Nonlinear System Identification

职业：统计信号处理方面的研究、教育和技术转让提案，重点是非线性系统辨识

• 批准号: 9703312
• 负责人: Guotong Zhou
• 金额: $ 5.13万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-10-01 至 2002-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9703312&HistoricalAwards=false
• 关键词: CAREER; Research; Education; Technology; Transfer

While linear approximations and models possess simplicity and tractability in analysis, most real life problems entail nonlinear effects. Nonlinear system of current interest in signal processing and communications are encountered with satellite links, high-speed telephone channels, power amplifiers, magnetic and optical recording systems. This research is exploring novel techniques for identification, performance analysis, and mitigation of nonlinear systems. The goal is to use state-of-the-art statistical signal processing tools to analyze long-standing issues in nonlinear modeling and applications to tele- communications problems. The present approach is taking a fresh look at the traditional Volterra model and exploiting carefully designed cyclostationary inputs that result in closed-form kernel expressions with input-output data. Such solutions will apply to finite memory Volterra models of any order, reduce computational complexity and noise effects even with relatively short data records, and allow for analytical performance evaluation using variance expressions. The investigator plans to simulate and test the resulting algorithms using real data received from satellite links where amplifiers are driven to the nonlinear saturation region. She is also studying blind nonlinear Volterra system identification that relies on single or multi-sensor data only. The potential of blind nonlinear channel estimation and equalization is of paramount importance when no training inputs are available, or when new communicators join a multiport broadcast and transmission cannot be interrupted for training.

虽然线性近似和模型在分析中具有简单性和易操作性，但大多数现实生活中的问题都需要非线性效应。目前在信号处理和通信中感兴趣的非线性系统在卫星链路、高速电话信道、功率放大器、磁和光记录系统中遇到。这项研究正在探索识别、性能分析和缓解非线性系统的新技术。目标是使用最先进的统计信号处理工具来分析在非线性建模和远程通信问题的应用中长期存在的问题。目前的方法是对传统的Volterra模型进行全新的审视，并利用精心设计的循环平稳输入，从而产生具有输入输出数据的封闭形式的核表达式。这样的解决方案将适用于任何阶数的有限记忆Volterra模型，即使在数据记录相对较短的情况下，也可以降低计算复杂性和噪声影响，并允许使用方差表达式进行分析性能评估。研究人员计划使用从卫星链路接收的真实数据来模拟和测试所产生的算法，在卫星链路中，放大器被驱动到非线性饱和区。她还在研究仅依赖单传感器或多传感器数据的盲非线性Volterra系统辨识。当没有可用的训练输入时，或者当新的通信器加入多端口广播并且不能中断传输以进行训练时，盲非线性信道估计和均衡的潜力是至关重要的。