Eigen decomposition

Adaptive Clutter Supperssion Algorithm Based on Fast Eigen Decomposition

基于快速特征分解的自适应杂波抑制算法

In this paper , by developing a method for updating eigen decomposition , we proposed an incremental Principal Component Analysis .

本文提出并推导了特征分解的校正算法，并以此为基础，实现了增量学习的主成分分析方法。

The algorithm uses the array geometries to construct a matrix and then obtain the required signal subspace via the eigen decomposition of the constructed matrix .

该算法首先利用阵列结构的特点构建一个矩阵，然后对其进行特征值分解得到信号子空间。该算法不需要谱峰搜索，能够很好地估计参数。

An auxiliary matrix including both the coded symbol sequences and DOAs is derived , on which the eigen decomposition is performed to obtain the DOAs of different users .

该算法通过构筑蕴涵用户编码符号序列和波达方向的辅助矩阵，对其进行特征分解，得到了每个用户波达方向的闭式解。

Then , to solve the problems existing in the H / α classification , a new method ( EKE method ), based on the eigen decomposition , Krogager decomposition and scattering entropy , is proposed .

在该框架下，针对H/α分类存在的问题，提出了一个基于特征分解、Krogager分解和散射熵的新方法（EKE方法）。

Based on subspace methods , by performing eigen decomposition on this auxiliary matrix , the closed form solution of both space frequency channel and DOA can be obtained simultaneously for all active users within one macrocell .

利用子空间方法对辅助矩阵进行特征分解，实现了对同步多载波码分多址系统上行空频信道和波达方向的联合盲估计。

The algorithm depends on neither the definite signal structure nor the assumption of channel model , it constructs a new matrix called spatial signature matrix ( SS matrix ) in cumulant domain , and estimates the multiuser spatial signature through eigen decomposition of SS matrix .

该算法既不依赖于信号的的具体结构也不依赖于信道的模型假设，构造了累量域空间特征矩阵，通过对空间特征矩阵的特征分解得到各用户信号的空间特征估计。

The traditional procedure of conic fitting utilizes the standard Eigen Value Decomposition ( EVD ) algorithm .

传统的二次曲线拟合使用标准特征值分析算法。

The camera intrinsic matrix with five parameters is obtained through eigen value decomposition of the infinite homography matrix .

通过对无穷远单应性矩阵的特征分解，计算5参数摄像机内参数阵。

The image sequence is acquired by a single camera . The camera intrinsic matrix with five parameters is obtained through eigen value decomposition of the infinite homography matrix .

人体运动跟踪系统处理的是由摄像机摄入的视频图像序列。通过对无穷远单应性矩阵的特征分解，计算5参数摄像机内参数阵。

Array Signal Eigen - space Decomposition and its Application in Beamforming

阵列信号特征空间分解及其在波束形成中的应用

Eigen vector power spectrum estimation is based on matrix eigen decomposition .

特征向量法功率谱估计是基于矩阵分解的一种功率谱估计的非参数方法。