自适应信号处理是统计信号处理的一个重要组成部分。当我们所处理信号的统计特征不确定或者信号内在非平稳时，自适应信号处理方法比传统的固定信号处理方法能提供更优越的性能，同时自适应信号处理也给出了一些传统信号处理所无法提供的能力。自适应信号处理理论已广泛地应用于通信、控制、雷达、声纳、生物医学等各种领域。因而学习和掌握自适应信号处理的理论具有重要的意义。本课程全面讲授自适应信号处理的数学基础、基本结构和基本算法，内容包括经典的维纳滤波、卡尔曼滤波以及相应的算法如最小均方算法（LMS）、递归最小平方算法（RLS）、阶数递归自适应滤波等，也包括日趋成熟的时变系统跟踪、盲解卷、后向传播学习、径向基函数网络等理论。

The subject of adaptive signal processing constitutes an important part of statistical signal processing. Whenever there is a requirement to process signals that result from operation in an environment of unknown statistics or one that is inherently nonstationary, the use of an adaptive processing offers a highly attractive solution to the problem as it provides a significant improvement in performance over the use of traditional fixed signal processing. Furthermore, the use of adaptive signal processing provides new signal processing capabilities that would not be possible otherwise. Also adaptive signal processing has been successfully applied in such diverse fields as communications, control, radar, sonar, and biomedical engineering, among others. So it is useful for post-graduated students to learn and master the theory of adaptive signal processing. In this course the mathematical fundaments, basic structures, fundamental algorithms of adaptive signal processing will be introduced. The content covers from traditional Wiener filters, Kalman filters, least-mean-square adaptive filters, recursive least-square adaptive filters, and order-recursive adaptive filters to developing theories as tracking of time-varying systems, blind deconvolution, back-propagation learning and so on.