Description: 功率谱 welch 方法
there is a simple demo for non parameteric spectral estimation methods-Welch method of power spectrum there is a simple demo for non parameteric spectral estimation methods Platform: |
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Author:lxp |
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Description: 分别用非参数化谱估计中的直接法，间接法和加窗法对离散随机过程的已零均值化的N个数据样本估计了离散信号的谱密度。-Were non-parametric spectral estimation of the direct method, indirect method and windowing method, discrete stochastic process has zero mean the data of the N-sample estimates of the spectral density of discrete signals. Platform: |
Size: 1024 |
Author:王会彦 |
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Description: Scaled Lab Helicopter Experimental Analysis.
Spectral Estimation
Non Parametric Transfer Function Estimation
Model Estimation
MATLAB Code and experimental Data Platform: |
Size: 5352448 |
Author:KOSTAS |
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Description: 实验目的：
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 about the use of experimental verification of the conclusions of the book, to master a variety of spectral analysis methods, learn experimental design and experimental results. Experiment content: applied to the spectral analysis methods, including: 1) non-parametric methods: periodogram (direct method), BT method (indirect method), Welch average periodogram 2) parameterization: RELAX, Capon 3) spatial spectrum estimation: common of DOA method (Capon) Platform: |
Size: 1024 |
Author:王凯 |
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Description: 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 use of experimental verification of the conclusions of the book, to master a variety of spectral analysis methods, learn experimental design and experimental results. Experiment content: applied to the spectral analysis methods, including: 1) non-parametric methods: periodogram (direct method), BT method (indirect method), Welch average periodogram 2) parameterization: RELAX, Capon 3) spatial spectrum estimation: common of DOA method (Capon) Platform: |
Size: 1024 |
Author:王凯 |
Hits:
Description: 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 about the use of experimental verification of the conclusions of the book, to master a variety of spectral analysis methods, learn experimental design and experimental results. Experiment content: applied to the spectral analysis methods, including: 1) non-parametric methods: periodogram (direct method), BT method (indirect method), Welch average periodogram 2) parameterization: RELAX, Capon 3) spatial spectrum estimation: common of DOA method (Capon) Platform: |
Size: 1024 |
Author:王凯 |
Hits:
Description: 最新、最全“高阶谱分析工具箱”，包括全部教程和DEMO.-There is much more information in a stochastic non-Gaussian or deterministic signal than is conveyed by its autocorrelation and power
spectrum. Higher-order spectra which are defined in terms of the higher-order moments or cumulants of a signal, contain this additional information. The Higher-Order Spectral Analysis (HOSA) Toolbox provides comprehensive higher-order spectral analysis capabilities for signal processing applications. The toolbox is an excellent resource for the advanced researcher and the practicing engineer, as well as the novice
student who wants to learn about concepts and algorithms in statistical signal processing.
The HOSA Toolbox is a collection of M-files that implement a variety of advanced signal processing algorithms for the estimation of cross- and auto-cumulants (including correlations), spectra and olyspectra,bispectrum, and bicoherence, and omputation of time-frequency
distributions. Based on these, algorithms for parametric and non-parametric blin Platform: |
Size: 2880512 |
Author:Peng Lv |
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Description: Multispectral remotely sensing imagery with high
spatial resolution, such as QuickBird, IKONOS satellite
imagery or Aerial imagery, especially in urban scenes, often
perform spectral variations and rich details within a category,
resulting in a poor accuracy of classification. To seek an efficient
solution, this paper presents a non-parametric and variational
multiple level set model by a joint use of Aerial image and two
products, digital terrain model (DTM) and digital surface model
(DSM), directly or indirectly derived raw LiDAR (Light
Detection And Ranging) 3D point cloud data. Proposed model is
to minimize an energy function. The energy includes two terms.
First term is mainly image-based energy which introduces Parzen
Window density estimation technique in the multiple level set
framework. To make up the disadvantages-Multispectral remotely sensing imagery with high
spatial resolution, such as QuickBird, IKONOS satellite
imagery or Aerial imagery, especially in urban scenes, often
perform spectral variations and rich details within a category,
resulting in a poor accuracy of classification. To seek an efficient
solution, this paper presents a non-parametric and variational
multiple level set model by a joint use of Aerial image and two
products, digital terrain model (DTM) and digital surface model
(DSM), directly or indirectly derived raw LiDAR (Light
Detection And Ranging) 3D point cloud data. Proposed model is
to minimize an energy function. The energy includes two terms.
First term is mainly image-based energy which introduces Parzen
Window density estimation technique in the multiple level set
framework. To make up the disadvantages Platform: |
Size: 2544640 |
Author:yangs |
Hits:
Search - Non-parametric Spectral Estimation