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: 9206829
• 负责人: Chrysostomos Nikias
• 金额: $ 15.42万
• 依托单位: University of Southern California
• 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=9206829&HistoricalAwards=false
• 关键词: Adaptive; Signal; Processing; Algorithms; Based

Nikias This is a collaborative research project with John G. Proakis (Northeastern University). 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.

Nikias 这是与 John G. Proakis（东北大学）的合作研究项目。 研究重点是基于非线性性能标准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.使用高阶统计量的决策反馈均衡器； 3、信号为高斯分布时ML型和MAP型的联合估计和检测算法； 4.当信号经历了严重的幅度和相位失真时，自适应多通道信号恢复算法； 5.基于神经网络和Volterra滤波器的盲反卷积算法。 特别强调对算法的数值特性和性能的研究。 正在考虑的性能指标包括作为信噪比函数的恢复序列中的错误概率、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。 新算法正在考虑在数字通信、语音、图像处理和地球物理学中应用。