chapter21

在对目标进行跟踪时，由于目标的运动方程和观测方程的非线性性，使得滤波时采用传统的卡尔曼滤波器时存在较大误差，不敏卡尔曼滤波器很好的避免了这一点。(Tracking of targets, because the objectives and observation equations of motion of nonlinear equations, and makes filtering when using the traditional Kalman filter when there is a big error, not a very good filter敏卡尔曼avoid this .)

A fast customizable function for locating and measuring the peaks in noisy time-series signals. Adjustable parameters allow discrimination of "real" signal peaks from noise and background. (A fast customizable function for locating and measuring the peaks in noisy time-series si gnals. Adjustable parameters allow discrimin ation of the "real" signal peaks from noise and back ground.)

提出一种多描述分层语音编码器，基于正弦和等效矩形带宽模型，将正弦和噪声模型参数根据能量大小进行适当分配以得到不同的描述和感知层(A layered multiple description speech coder based on the sinusoidal model and the equivalent rectangular bandwidth will be sinusoidal and noise model parameters in accordance with the energy distribution of the size appropriate to get different descriptions and perceptual layer)

说明：  压缩感知compressivesensing2010jan12最新资料,研究其在图像处理模式识别 视觉编码及其硬件实现等，该研究成为2009-2010年国家自然科学基金重要的研究热点，我也刚刚开始涉及该信号处理基础方向(compressive sensing documents search in Feb 2010)

说明：  画出函数的ROC曲线，precision/recall 曲线(function [prec, tpr, fpr, thresh] = prec_rec(score, target, varargin) PREC_REC- Compute and plot precision/recall and ROC curves.)

remove noise from multiple noisy images

设计一个模糊控制器，通过调节阀门将水位稳定在固定点附近。(Design a fuzzy controller, water level by adjusting the valve will stabilize at a fixed point nearby.)

In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discretedata linear filtering problem [Kalman60]. Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and application, particularly in the area of autonomous or assisted navigation. A very “friendly” introduction to the general idea of the Kalman filter can be found in Chapter 1 of [Maybeck79], while a more complete introductory discussion can be found in [Sorenson70], which also contains some interesting historical narrative.(In 1960, R. E. Kalman published his famous paper describ ing a recursive solution to the discretedata li near filtering problem [Kalman60]. Since that time, due in large part to advances in digital computi Vi, the Kalman filter has been the subject of extens ive research and application. particularly in the area of autonomous or assis ted navigation. A very "friendly" introductio n to the general idea of the Kalman filter can be f ound in Chapter 1 of [Maybeck79] while a more complete introductory discussion can be found in [Sorenson70] which also contains some interesting historic al narrative.)

用M文件S函数编写的变步长的连续时间变量的仿真(This example S-function illustrates how to create a variable step block in Simulink.)

说明：  Direct Sequence Spread spectrum