Research on Adaptive Signal Processing Methods

本文研究了基于信号高阶统计量的自适应信号处理算法的开发、测试和数学分析，以及它们的应用。在新算法的开发中，考虑了高阶统计域中不同的“良好性标准”和不同的自适应滤波实现。重点研究了算法的数值性质和性能。所考虑的性能度量包括作为SNR的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工作长度的影响。将新算法的性能与现有算法进行比较，以确定其优势和劣势。最后讨论了新算法在判决反馈均衡、盲均衡、噪声抵消、系统辨识等方面的应用。

This research deals with the development, testing and mathematical analysis of adaptive signal processing algorithms based on higher-order statistics of the signals involved in the adaptation process and their applications. Different "criteria of goodness" in higher-order statistics domains and different adaptive filter realizations are being considered in the development of the new algorithms. 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 work length effects. The performance of the new algorithms is compared to that of the existing ones to establish strengths and weaknesses. Finally, applications of the new algorithms are considered, e.g., in decision feedback equalization, in blind equalization, noise cancelling, and system identification.