tiaozhi

基于Matlab7_0PDE工具箱求解数学物理方程。直接应用Matlab7.0 的偏微分方程工具箱求解数学物理方程的方法(Based on equations of mathematical physics to solve Matlab7_0PDE toolbox. Matlab7.0 direct application of partial differential equations of mathematical physics equation solving toolbox approach)

The main idea behind this book, and the incentive for writing it, is that strong connections exist between adaptive filtering and signal analysis, to the extent that it is not realistic—at least from an engineering point of view—to separate them. In order to understand adaptive filters well enough to design them properly and apply them successfully, a certain amount of knowledge of the analysis of the signals involved is indispensable. Conversely, several major analysis techniques become really efficient and useful in products only when they are designed and implemented in an adaptive fashion. This book is dedicated to the intricate relationships between these two areas. Moreover, this approach can lead to new ideas and new techniques in either field.

toolbox for pattern recognition

压缩孔径编码 用于压缩感知成像 比较详尽适合初学者参考使用(Compression coding for compressed sensing aperture imaging using more detailed reference for beginners)

说明：  雷达信号相关内容，已打包成函数，bpsk，fsk，lfm等(Radar signal related content, packed into functions, BPSK, FSK, LFM, etc)

matlab入门级课件，内容简单易懂，值得一看！(matlab entry-level courseware, content easy to understand, worth a visit!)

用汇编语言编写卷积运算。Visual DSP++软件环境下使用三角波和正弦波卷积运算验证程序，与MATLAB仿真结果对比。 (Convolution written in assembly language. Visual DSP++ using the triangle wave and sine convolution validator software environment, compared with the MATLAB simulation results.)

GBP1仿真，用于雷达跟踪，包含卡尔曼滤波器(GBP1 simulation)

用MATLAB编写的svm源程序，可以实现支持向量机，用于特征分类或提取(Using MATLAB svm prepared by the source, can achieve a support vector machine for classification or feature extraction-MATLAB)

A method is presented for augmenting an extended Kalman filter with an adaptive element. The resulting estimator provides robustness to parameter uncertainty and unmodeled dynamics.(A method is presented for augmenting an ext Kalman ended with an adaptive filter element. T he resulting estimator provides robustness to parameter uncertainty and unmodeled dynamics .)