Adaptive Signal Processing Algorithms Based on Nonlinear Performance Criteria

基于非线性性能准则的自适应信号处理算法

• 批准号: 9115526
• 负责人: John Proakis
• 金额: --
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-03-01 至 1995-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9115526&HistoricalAwards=false
• 关键词: Adaptive; Signal; Processing; Algorithms; Based

Proakis This is a collaborative research project with Chrysostomos L. Nikias (University of Southern California). The research focuses on the development, testing and mathematical analysis of adaptive signal processing algorithms based on nonlinear performance criteria. Areas of research include: 1. blind deconvolution algorithms based on criteria with memory nonlinearity; 2. decision feedback equalizers using higher-order statistics; 3. joint estimation and detection algorithms of the ML-type and MAP-type when the signals are Gaussian distributed; 4. adaptive multichannel signal recovery algorithms when the signals have gone through severe magnitude and phase distortion; and 5. blind deconvolution algorithms based on neural networks and Volterra filters. Special emphasis is placed on the investigation of the numerical properties and performance of the algorithms. Performance metrics being considered include probability of error in the restored sequence as a function of SNR, sensitivity to signal statistics and data conditioning, rate of convergence, choice of sliding windows and finite length effects. Applications of the new algorithms are being considered in digital communications, speech, image processing and geophysics.

Proakis 这是一个与Chrysostomos L. Nikias（南加州大学）。 研究的重点是基于非线性性能准则的自适应信号处理算法的开发、测试和数学分析。 研究领域包括：1。 基于记忆非线性准则的盲反卷积算法; 2. 使用高阶统计的判决反馈均衡器; 3. 信号为高斯分布时的ML型和MAP型联合估计检测算法; 4. 当信号已经经历严重幅度和相位失真时的自适应多通道信号恢复算法;以及5. 基于神经网络和沃尔泰拉滤波器的盲反卷积算法。 特别强调的是放在调查的数值性质和性能的算法。 正在考虑的性能指标包括恢复序列中作为SNR的函数的错误概率、对信号统计和数据调节的敏感性、收敛速度、滑动窗口的选择和有限长度效应。 新算法的应用正在考虑在数字通信，语音，图像处理和电子物理学。