Programmable Blind Adaptive Multivariate Filtering

可编程盲自适应多元滤波

• 批准号: 9412732
• 负责人: William Gardner
• 金额: $ 18.57万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-07-01 至 1999-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9412732&HistoricalAwards=false
• 关键词: Programmable; Blind; Adaptive; Multivariate; Filtering

In wireless communications, including cellular communication systems, spread spectrum overlay systems, and signals intelligence applications, the degradation caused by rapidly time-varying multipath and unknown cochannel interference can be reduced by adaptive spatial filtering using antenna arrays. This research is pursuing a flexible framework for adapting a spatial filter without using a training signal, array calibration data, or knowledge of spatial characteristics of the desired or interfering signals. It is derived from two conceptually different perspectives: canonical correlation analysis from multivariate statistics, and the conditional maximum likelihood problem for a data-derived training signal obtained from a user-programmable transformation of the received data. The transformation can be programmed by the user to sort and separate the received signals on the basis of the differing degrees to which they exhibit one or more user-selected statistical properties. Many such useful transformations are being examined. The performance of the new method is being investigated analytically and by computer simulations to quantify its capabilities of signal separation, multipath mitigation, and interference rejection. Preliminary results suggest that the new method can converge very quickly to yield signal estimates that are comparable to those obtained by the MSE method that uses perfectly known training signals. More generally, the objective of this research is to develop and evaluate a new and very general approach to programmable blind adaptive multivariate filtering that has broad application to sensor array processing in addition to wireless communications.

在无线通信中，包括蜂窝通信系统、扩频覆盖系统和信号智能应用，由快速时变多径和未知共信道干扰引起的退化可以通过使用天线阵列的自适应空间滤波来减少。这项研究正在寻求一种灵活的框架来适应空间滤波器，而不使用训练信号、阵列校准数据或所需或干扰信号的空间特征知识。它是从两个概念上不同的角度衍生出来的：来自多元统计的典型相关分析，以及来自接收数据的用户可编程转换获得的数据派生训练信号的条件最大似然问题。用户可以对转换进行编程，以便根据接收到的信号显示一个或多个用户选择的统计属性的不同程度对其进行排序和分离。许多这样有用的转变正在研究中。新方法的性能正在进行分析研究，并通过计算机模拟来量化其信号分离、多径抑制和干扰抑制的能力。初步结果表明，新方法可以非常快速地收敛，产生的信号估计与使用完全已知训练信号的MSE方法获得的信号估计相当。更一般地说，本研究的目的是开发和评估一种新的和非常通用的可编程盲自适应多元滤波方法，该方法在传感器阵列处理和无线通信中具有广泛的应用。