Adaptive Signal Processing Algorithms Based on Nonlinear Performance Criteria

这是与Chrysostomos L. Nikias（南加州大学）的合作研究项目。研究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. 基于高阶统计量的决策反馈均衡器3. 当信号为高斯分布时，ml型和map型的联合估计和检测算法；4. 信号严重幅相失真时的自适应多通道信号恢复算法和5。基于神经网络和Volterra滤波器的盲反卷积算法。特别强调的是对算法的数值性质和性能的研究。考虑的性能指标包括恢复序列中作为信噪比函数的误差概率、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。目前正在考虑将新算法应用于数字通信、语音、图像处理和地球物理。

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.