搜索资源列表
lsarma
- 两步最小二乘arma法,k为算法中带估计的ar参数个数-Two-step least squares arma method, k is the algorithm with a number of parameters estimated ar
DOA_esprit
- 雷达信号到达角估计的MATLAB代码,进行了多参数的对比仿真和结果比较-Radar signal DOA estimation MATLAB code for the comparison of simulation and multi-parameter comparison results
Theestimatedsoundsignallungfatigue
- 采集肱二头肌做屈伸运动过程中的肌电信号,对其进行处理,进行时域和频域分析,并且计算积分肌电值,中位频率等参数,估计其疲劳过程-Collection do biceps muscle during flexion and extension signals, processes, time domain and frequency domain analysis, and calculating the integral EMG values, the median frequency and o
RMLE_system_identification_matlab
- 使用递推极大似然估计法辨识系统参数,这是《系统辨识》这门课程的程序,希望大家(特别是上这门课需要写论文的同学)能够用得上-Using maximum likelihood estimation method recursive identification system parameters, this is the " system identification" procedure for this course, I hope you (especially on thi
CSD_Detect_Eval1
- Matlab编写的循环谱计算程序,并对谱参数进行检测和估计,如载频估计,时宽估计,幅度估计等-Matlab computer program written in cyclic spectrum, and spectral parameters for detection and estimation, such as carrier frequency is estimated at wide estimated range estimation
LMS-Lattice-Algorithm
- 研究了自适应参数谱估计的基本原理,提出了基于最小均方误差(MMSE)准则的LMS格型滤波算法实 现自适应参数谱估计的方法。通过对仿真测试信号和线性调频信号的计算表明,该方法能够在改善谱分辨率的 同时提高运算效率,满足遥测速变信号的处理和分析计算的要求-This paper discusses the principle of adaptive parametric spectrum estimation.It provides an approach to es— timate po
mixturecode2
- 自适应的选择高斯混合模型个数,并用EM算法估计参数-Adaptive selection of the number of Gaussian mixture model and estimated parameters using EM algorithm
jiyinzhouqi
- 利用自相关函数估计基音周期,函数jiyinzhouqi,输入参数是分析语音文件名和帧移,帧移在5~10ms-function jiyinzhouqi is used for estimate the period of fundamental tone.
psp1
- 使用逐幸存算法,对有码间串扰的信道参数和进行估计发送信息进行估计-Algorithm used by survivors, for there is intersymbol interference channel to send information to estimate parameters and to estimate the
EM
- 利用Matlab编程验证用EM算法估计的高斯混合模型的相关参数的性能。-Validate the use of Matlab programming estimated using EM algorithm for Gaussian mixture model parameters related to the performance.
GREY
- 连续的系统灰色控制改善了控制性能,并提高了鲁棒性能,采用模型参数v粗略的估计出来,在加以补偿-Continuous gray control system to improve the control performance and improved robustness, using a rough estimate model parameters v out, to be compensated in
VB_RELS
- 用Vb实现的递推最小二乘法估计参数,有可视界面-Vb to achieve with the estimated parameters of recursive least squares method, a visual interface
Blind-identification
- 利用现代谱估计中的AR 模型求取仿真噪声信号和真实噪声信号的模型参数,然后利用这些参数求得信号的功率谱图,通过功率谱图定性地描述主噪声源和次噪声源。在利用信号功率谱图定性描述噪声源后,进一步地利用曲线相似度和曲线关联度定量的识别主噪声源和次噪声源-Use of modern spectral estimation of AR model to strike a realistic simulation of the noise signal and noise model parameters,
212
- 半参数线性回归模型的最小一乘核估计Semi-parametric linear regression model for the smallest estimated by nuclear-Semi-parametric linear regression model for the smallest estimated by nuclear
clean
- 研究上课所讲谱分析方法,利用实验验证书中的结论,掌握各种谱分析方法,学会实验设计和实验结果分析。 所应用到的谱分析方法,包括: 1) 非参数化方法:周期图(直接法)、BT法(间接法),Welch平均周期图法 2) 参数化方法: RELAX、Capon 3) 空间谱估计:常见的DOA方法(Capon) -Experimental Objective: To study methods of spectral analysis class talking about the us
relax
- 研究上课所讲谱分析方法,利用实验验证书中的结论,掌握各种谱分析方法,学会实验设计和实验结果分析。 实验内容: 所应用到的谱分析方法,包括: 1) 非参数化方法:周期图(直接法)、BT法(间接法),Welch平均周期图法 2) 参数化方法: RELAX、Capon 3) 空间谱估计:常见的DOA方法(Capon) -Experimental Objective: To study methods of spectral analysis class talking abou
task22
- 递推极大似然估计法辨识系统参数,输入是M序列,周期为N=24-1。利用递推极大似然算法对系统参数进行辨识-Recursive maximum likelihood estimation method identification system parameters, input M-sequence, period N = 24-1. Using recursive maximum likelihood algorithm for identification of system paramet
stationary_signal_analysis
- 平稳信号分析。包括经典功率谱估计、基于参数建模的功率谱估计以及基于非参数建模的功率谱估计。-Stationary signal analysis. Including the classic power spectrum estimation, based on parametric modeling of the power spectrum estimation and modeling based on non-parametric power spectrum estimation.
234234
- 基于非参数核密度估计的Copula函数选择原理.-Based on nonparametric kernel density estimation in the Copula function selection principle
arls
- 对于信号 ,其中w(n)为均值为0,方差为1的AWGN。n=1,2,…,128。 AR模型功率谱估计。假设AR(4),用LS方法估计AR参数,功率谱用freqz(1,LS_ar,1024,1)来验证。-For the signal, which w (n) is zero mean and variance 1 AWGN. n = 1,2, ..., 128. AR model power spectrum estimation. Assuming AR (4), with the