本章从电路的组成及其分类出发，介绍了电路模型的概念、求解电路模型的基本定律、电阻元件、电源元件的联接方式及其特点；在此基础上进一步介绍电路分析的常用方法：如等效变换、支路电流、结点电压、叠加原理、戴维宁定理等

理解 A/D 转换的原理与过程 了解 A/D 的基本参数及应用

盲系统辨识 盲源分离(BSS, Blind Source Separation) 独立分量分析 (ICA, Independent Component Analysis) 信道盲均衡 盲波束形成

 Introduction  Array signal model  Low resolution Methods  High resolution Methods  Applications  Future Directions

 S1. Introduction  S2. LS Method  S3. RLS algorithm  S4. Examples

 S1. Introduction  S2. Conventional Kalman filter  S3. Example  S4. RLS and Kalman filtering  S5. Square-root information filter  S6. Square-root covariance filter  Better numerical properties than the conventional one  The square-root RLS algorithms are special cases

 Steepest-descent algorithm  LMS algorithm  LMS for time-varying set-ups  LMS variants  Normalized LMS (NLMS)  LMS versus RLS convergence

 S.1 Prototype adaptive filter  Filter structure  Quadratic cost functions  S2. Wiener filter theory  MMSE criterion  Wiener-Hopf equations  Orthogonality principle  S3. Unrealizable Wiener filter

 Introduction  Periodogram and correlogram  Nonparametric methods  Parametric methods for Rational spectra  Parametric methods for line spectra  Filter-bank approach  Spatial methods

 S1. Filters: devices of signal processing  Classic Filter  Optimal Filter  Adaptive Filter  S2. Adaptive filtering applications  S3. Stochastic processes  Partial characteristic: Autocorrelation Matrix (ACM)  PSD  Linear parametric model  S4. Mean Square Information Space