 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

 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

8.1 representation of periodic sequences:the discrete fourier series 8.2 the fourier transform of periodic signals 8.3 properties of the discrete fourier series 8.4 fourier representation of finite-duration sequences: Definition of the discrete fourier transform 8.5 sampling the fourier transform(point of sampling) 8.6 properties of the fourier transform 8.7 linear convolution using the discrete fourier transform 8.8 the discrete cosine transform(DCT)

第四章快速傅立叶变换(FFT) 主要内容: 一、/DFT的问题及解决途径 二、FFT的原理和复杂性 三、按时间抽取的FFT算法 四、按频率抽取的FFT算法 五、离散傅立叶反变换的快速算法 六、线性调频z变换(chirp-z变换)算法 七、线性卷积的FFT算法

Introduction Ideally, the system parameters along with the signal variables have infinite precision taking any value between -oo and · In practice, they can take only discrete values within a specified range since the registers of the digital machine where they are stored are of finite length

◆理解按频率抽选的基-2FFT算法的算法原理、运算流图、所需计算量和算法特点 ◆理解IFFT算法 ◆了解混合基、分裂基和基-4FFT算法 ◆了解CZT算法 ◆理解线性卷积的FFT算法及分段卷积方法

第3章离散傅里叶变换(DFT) 3.1离散傅里叶变换的定义 3.2离散傅里叶变换的基本性质 3.3频率域采样 3.4DFT的应用举例

第4章快速傅里叶变换(FFT) 4.1引言 4.2基2FFT算法 4.3进一步减少运算量的措施 4.4分裂基FFT算法 4.5离散哈特莱变换(DHT)

◆理解傅里叶变换的几种形式 ◆了解周期序列的傅里叶级数及性质,掌握周期卷积过程 ◆理解离散傅里叶变换及性质,掌握圆周移位、共轭对称性,掌握圆周卷积、线性卷积及两者之间的关系 ◆了解频域抽样理论 ◆理解频谱分析过程 ◆了解序列的抽取与插值过程

3.1 Discrete-Time Fourier Transform Definition- The discrete-time Fourier transform (DTFT) X(eio) of a sequence x[n] is given by jae In general,() is a complex function of the real variable and can be written as X(eio) Xre(eio) +j Xim(eio)