CA_simulation

matlab中gui界面设计，不同编码调制条件下BER曲线的显示及对比。(in matlab gui interface design, coding and modulation under different BER curves display and contrast.)

卡尔曼滤波器（kalman filter）的matlab代码(kalman filter matlab code)

為了幫助大夥兒升級，我們為現在的Apache使用者提供了一份重要訊息的文檔說明。這些只是一些簡要說明，你可以從新特性文檔或src/CHANGES文件中得到更多訊息。

摘要:计算机动态仿真是混沌应用研究的重要方法"该文介绍了环境下的可视化建模与动态仿真步骤(Abstract: The computer simulation is a chaotic dynamic applied research an important means of )

根据雷达信号到达时间用PRI方法进行雷达信号分选的一段matlab代码(Radar signal processing)

MUSIC算法[1] 是一种基于矩阵特征空间分解的方法。从几何角度讲，信号处理的观测空间可以分解为信号子空间和噪声子空间，显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成，噪声子空间则由协方差矩阵中所有最小特征值（噪声方差）对应的特征向量组成。MUSIC算法就是利用这两个互补空间之间的正交特性来估计空间信号的方位。噪声子空间的所有向量被用来构造谱，所有空间方位谱中的峰值位置对应信号的来波方位。MUSIC算法大大提高了测向分辨率，同时适应于任意形状的天线阵列，但是原型MUSIC算法要求来波信号是不相干的。(algorithm MUSIC is a method based on matrix feature space decomposition. From a geometric point of view, the signal processing of the observation space can be decomposed into signal subspace and noise subspace, it is clear that these two spaces are orthogonal. Signal subspace by the array received data covariance matrix and signal corresponding to the feature vector and noise subspace is by the covariance matrix of all minimum feature values (noise variance) corresponding to the feature vectors are composed. MUSIC algorithm is to use the orthogonal characteristics of these two complementary space to estimate the azimuth of spatial signals. All vectors of the noise subspace are used to construct spectral, and the peak position in all spatial azimuth spectra correspond to the direction of arrival of the signal. MUSIC algorithm greatly improves the direction finding resolution, and at the same time it is suitable for the antenna array of any shape, but the prototype MUSIC algorithm requir)

小波分析理论与matlab2007配套程序(Wavelet analysis theory and matching procedure matlab2007)

这个程序主要是基于turbo编码的编解码实现程序。(This procedure is based mainly on the turbo-coded codec implementation procedures.)

TST matlab 代码可以预测时间序列中的结构性变动点，以及用来进行样本外预测。(TSt matlab code can be useful for identifying structural changes in time series and particularly be helpful for forecasting.)

A little example for Audiofingerprinting in Matlab that shows how shazam took it first steps