Higher Order Statistical Signal and Image Processing and Analysis

高阶统计信号和图像处理与分析

• 批准号: 9312559
• 负责人: Jitendra Tugnait
• 金额: $ 14.69万
• 依托单位: Auburn University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-04-01 至 1997-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9312559&HistoricalAwards=false
• 关键词: Higher; Order; Statistical; Signal; Image

This research is concerned with development, analysis and evaluation of algorithms for signal/image processing in addition to, or in lieu of, the usual second order statistics. Both time series (only system output is observed) and system identification (both input and output are observed) formulations are being considered; and both one-dimensional and multidimensional signals and systems are being investigated. Whereas the second order statistics of signals are a function of only the underlying system transfer function magnitude, higher order Statistics (HOS) of the data carry useful information about the phase characteristics of the underlying signal/system which is crucial in deconvolution problems such as those arising in digital communication channel equalization, seismic wavelet processing, and image restoration, analysis and synthesis. Time domain as well as frequency domain methods using higher order cumulant functions and higher order cumulant spectra (or higher order integrated cumulant spectra), respectively, are being pursued. Among the applications being investigated are: (i) image texture synthesis and classification, (ii) differential time-delay and doppler estimation in unknown spatially correlated Gaussian noise, (iii) blind deconvolution with unknown, possibly nonminimum phase, channels including image restoration, and (iv) system identification with noise inputs.

本研究关注的是开发，分析和评价的信号/图像处理算法，除了，或代替，通常的二阶统计。 时间序列（只观察系统输出）和系统识别（观察输入和输出）公式正在考虑中，一维和多维信号和系统正在研究中。 然而，信号的二阶统计量仅是底层系统传递函数幅度的函数，数据的高阶统计量（HOS）携带关于底层信号/系统的相位特性的有用信息，这在诸如数字通信信道均衡、地震子波处理以及图像恢复、分析和合成中出现的反卷积问题中是至关重要的。 分别使用高阶累积量函数和高阶累积量谱（或高阶积分累积量谱）的时域和频域方法正在被追求。 正在研究的应用程序是：（一）图像纹理合成和分类，（二）差分时间延迟和多普勒估计在未知的空间相关的高斯噪声，（三）盲反卷积未知，可能非最小相位，通道，包括图像恢复，和（四）系统识别与噪声输入。