搜索资源列表
LDPC_matlab
- LDPC经过高斯白噪声信道的的编译码仿真程序-LDPC Gaussian white noise through the channel of the codec simulation program
ffs
- 在matlab环境下仿真随机信号，括号内为内容（正弦信号、白噪声信号、正弦信号附加白噪声三种信号形式） 的代码，计算该随机信号的相关函数和功率谱-In the matlab simulation environment, random signals, the contents of brackets (sinusoidal signal, white noise signal, sine wave signal form of three additional white noise
suiji
- 随机信号分析实验之白噪声测试，白噪声信号的特性，包括均值、方差、相关函数、概率密度、频谱及功率谱密度等。- White noise of test random signal analysis experiment, white noise signal characteristic, including average value , variance, correlation function, probability density, frequency spectrum and powe
sim
- 关于matlab信道仿真中经常用到的信道噪声函数，包括高斯白噪声，瑞利信道，赖斯信道的仿真-Channel simulation on matlab frequently used function of channel noise, including Gaussian white noise, Rayleigh Road, Lai Sixin Road Simulation
signal
- 随机信号处理的仿真，一个余弦信号加高斯白噪声，用自相关函数恢复。-Random signal processing simulation, a cosine signal plus Gaussian white noise with autocorrelation function recovery.
Gauss_noise_generation
- 能够产生高斯白噪声，有色噪声和一般噪声。用于matlab仿真实验。-Generated Gaussian white noise, colored noise and general noise.
single-generate
- 产生典型的几种信号波形，如方波，三角波，白噪声等。-A typical signal waveforms generated several, such as square, triangle, white noise.
system_identification
- 系统辨识的实验代码，包括产生白噪声程序、各种最小二乘法程序等等，包含所有源代码，用C++写的。-Experimental system identification code, including the generation of white noise process, a variety of least squares procedures, etc., including all source code, using C++ written in.
levinsonf
- 信号为两个正弦信号加高斯白噪声，用levinson递推法进行功率谱估计。-Signal for two sinusoidal signal plus Gaussian white noise, with levinson recursive power spectrum estimation method.
16qam_channel_encode
- 一、 本程序采用16QAM调制方式，对一串2进制信源进行调制，采用（7，4）循环码对信源做信道编码，用升余弦滚降函数进行基带调制，再调到高频信道；在信道上加入高斯白噪声，运用匹配滤波器解调，画出解调星座图，运用最小欧氏距离译码判决，再对信号进行信道解码，画出采用信道编码技术和不采用信道编码技术的误比特率图。-First, this program uses 16QAM modulation mode, for a string of two binary source is modulated,
Identifying_pulse_response
- 系统辨识课上做的利用相关分析法辨识脉冲响应的实验。包括用M序列生成白噪声，计算互相关函数得到脉冲响应估计值等等，比较基础-Experiment about Identifying the pulse response with a method of correlative analysis in my class on system identification. It contains using M-sequence to generate white noise, bu calculat
eg-fft
- fft1．用Matlab产生正弦波,矩形波,以及白噪声信号，并显示各自时域波形图 2．进行FFT变换，显示各自频谱图，其中采样率，频率、数据长度自选 3．做出上述三种信号的均方根图谱,功率图谱,以及对数均方根图谱 4．用IFFT傅立叶反变换恢复信号，并显示恢复的正弦信号时域波形图-fft1. Using Matlab generated sine wave, rectangular wave, as well as the white noise signal, and displa
FDD
- 数据频域分解法，基于结构的环境白噪声激励响应数据，识别结构的频率和振型-The frequency domain decomposition method, based on the structure of the environmental white noise stimulus-response data, identify the structure of the frequencies and mode shapes
Untitled
- 关于高斯白噪声下信源数目的估计程序-On the Gaussian white noise source under the estimate of the number of procedures
C_LMS
- 研究利用LMS自适应均衡器纠正存在加性白噪声的信道的畸变。讨论步长参数 和特征值扩散度 对学习曲线的影响。-To examine the use of LMS adaptive equalizer to correct the existence of additive white noise distortion channel. Discussion of step parameters and eigenvalues of the proliferation of degrees of i
C_RLS
- 研究利用RLS算法自适应均衡器纠正存在加性白噪声的信道的畸变。讨论特征值扩散度 对学习曲线的影响。 比较RLS算法和LMS算法在不同信噪比情况下的学习曲线。 -RLS algorithm using adaptive equalizer to correct the existence of additive white noise distortion channel. Eigenvalue discussion diffusivity impact on the learning cur
createwhitenoise
- 产生白噪声的方法，调试通过，数据和代码都齐全。-Generated white noise method, debug through, data and code are complete.
linearroot
- 求根MUSIC算法，仿真中使用的阵列为均匀阵列，阵列数为8，接收信号数为2个，噪声为高斯白噪声。此算法是对经典MUSIC算法的改进，估计精确度更高。-Root MUSIC algorithm, the use of simulation for the uniform array of arrays, arrays of 8 to receive the signal number 2, noise is Gaussian white noise. This algorithm is the c
LMSyuyin
- 本文将现实中采集到的一段语音加上了高斯白噪声，利用了LMS算法，将噪声滤除，仿真效果良好-This article will be collected in reality a section of voice coupled with a Gaussian white noise, using the LMS algorithm, the noise filtering, simulation results
fbh
- 使用FFT分析方波信号的频谱，讨论采样点数、采样频率对谱分辨率的影响。对数据叠加白噪声后再次分析实验结果。-Square-wave signal using the FFT spectrum analysis to discuss the sampling points, the sampling frequency of the impact of spectral resolution. White noise superimposed on the data after analysis