搜索资源列表
K_smooth
- The subroutines glkern.f and lokern.f use an efficient and fast algorithm for automatically adaptive nonparametric regression estimation with a kernel method. Roughly speaking, the method performs a local averaging of the observations when es
fft
- 本文件包含三个小程序，分别为用fft实现功率谱估计、实现快速卷积和快速相关。对了解fft在c中的编程很有帮助-This document contains three procedures were used to achieve power spectrum estimation fft, fast convolution and fast correlation. Fft to know programming in c in the helpful
MP_DOA
- 针对多径效应的影响，提出了一种基于矩阵束的MIMO 雷达低仰角快速估计方法。该方法同时考虑了发射多径信号和接收多径信号，采用单样本数信号矢量构造了一个前后向矩阵束，并利用两个酉矩阵对该矩阵束进行降维处理，最后采用广义特征值分解的总体最小二乘法来估计目标角度。算法不需要估计数据协方差矩阵，可在低 信噪比和单样本数情况下，有效地克服多径效应，实现同时多目标低仰角估计，相比最大似然算法，避免了谱峰搜索，计算量小。仿真结果验证了该算法的有效性。-To overcome the multipath e
kde
- kernel density estimation based on fast marching method
benlui
- 利用贝叶斯原理估计混合logit模型的参数，gmcalab 快速广义的形态分量分析，可以实现模式识别领域的数据的分类及回归。- Bayesian parameter estimation principle mixed logit model, gmcalab fast generalized form component analysis, You can achieve data classification and regression pattern recognition.
L-D
- 现代谱估计参数谱估计（AR模型）做频谱分析，基于Levison-Durbin快速递推法和Burg算法的源程序。 -Modern spectral estimation spectral estimation parameters (AR model) to do spectrum analysis, based on Levison-Durbin fast recursive method and the Burg algorithm source.
liefang
- gmcalab 快速广义的形态分量分析，ML法能够很好的估计信号的信噪比,wolf 方法计算李雅普诺夫指数。- gmcalab fast generalized form component analysis, ML estimation method can be a good signal to noise ratio, wolf calculated Lyapunov exponent.
nounun
- gmcalab 快速广义的形态分量分析，利用贝叶斯原理估计混合logit模型的参数，用于信号特征提取、信号消噪。- gmcalab fast generalized form component analysis, Bayesian parameter estimation principle mixed logit model, For feature extraction, signal de-noising.
CS-recovery-LevelSet-Normals
- 压缩感知恢复算法，使用新的范数来提升图像恢复能力，包含论文和代码。-We propose a compressive sensing algorithm that exploits geometric properties of images to recover images of high quality few measurements. The image reconstruction is done by iterating the two following steps: 1) e