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subspace-tracking
相关语句
  子空间跟踪
     Subspace-Tracking Channel Estimation for Training-Sequence-Based OFDM Systems with Transmitter Diversity
     基于训练序列的发射分集OFDM系统的子空间跟踪信道估计
短句来源
     Subspace-Tracking Channel Estimation for Pilot-Symbol-Aided OFDM Systems with Transmitter Diversity
     基于导频的发射分集OFDM系统的子空间跟踪信道估计
短句来源
     This paper proposed an improved subspace-tracking algorithm by the Givens rotation based delay-subspace tracking and the RLS filtering based amplitudes tracking.
     为此,在建立通用参数化信道模型的基础上,提出一种子空间跟踪的改进算法.
短句来源
  “subspace-tracking”译为未确定词的双语例句
     In this paper, three subspace-tracking channel estimation algorithms for OFDM systems with transmitter diversity are proposed, and their performances are compared.
     该文提出了3种基于导频的发射分集正交频分复用(OFDM)系统的子空间幅度跟踪信道估计方法,并分析比较了其估计性能。
短句来源
  相似匹配句对
     Bi-iteration Subspace Tracking
     双迭代子空间跟踪
短句来源
     On Tracking
     关于统调
短句来源
     Several algorithms of subspace tracking are investigated.
     研究和分析了多种子空间跟踪算法.
短句来源
     Several algorithms of subspace tracking are analyzed.
     分析比较了多种子空间跟踪算法.
短句来源
     A METHOD OF RANK-1 SUBSPACE TRACKING
     秩-1子空间跟踪算法
短句来源
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A dificulty in state spase model system identification approaches is the high computation and storage burden. In this paper, a subspace tracking approach based on APEX algorithm is applied in state space model identification. The Neural network model of APEX algorithm can effectively reduce the computation cost andmake the state spce model identification algorithm available for real time application. We apply the state spacemodel identification algorithm obtained in time varying system identification.This approach...

A dificulty in state spase model system identification approaches is the high computation and storage burden. In this paper, a subspace tracking approach based on APEX algorithm is applied in state space model identification. The Neural network model of APEX algorithm can effectively reduce the computation cost andmake the state spce model identification algorithm available for real time application. We apply the state spacemodel identification algorithm obtained in time varying system identification.This approach is demonstrated in thesimulation and application in real data.

基于系统状态空间模型的系统辨识方法的一个困难在于算法具有较大的运算量和存储量。本文将基于APEX算法的子空间跟踪方法引入辨识算法。APEX算法的神经网络实现可以有效地减少辨识算法的运算量和存储量。本文将得到的算法应用于时变系统的辨识,仿真结果和应用于实际数据的结果验证了方法的有效性。

Channel vector estimation is a key to smart antenna. Recently some estimation and recursive tracking algorithms are produced, however, the algorithms are rather complex, unstable and are not easy to be realized by VLSI. The objective of this paper is to study the performance of a noniterative subspace tracking algorithm in mobile CDMA, including tracing's mismatch loss, convergence and system capacity. The array response vector of the uplink channel can be estimated by computing the principal eigenvector of...

Channel vector estimation is a key to smart antenna. Recently some estimation and recursive tracking algorithms are produced, however, the algorithms are rather complex, unstable and are not easy to be realized by VLSI. The objective of this paper is to study the performance of a noniterative subspace tracking algorithm in mobile CDMA, including tracing's mismatch loss, convergence and system capacity. The array response vector of the uplink channel can be estimated by computing the principal eigenvector of data covariance matrix, which can be decomposed into an one\|dimension signal subspace and multiple\|dimension noise subspace. By averaging the noise subspace, we convert the principal eigenvector update into 2×2 eigen problem, which largely reduce the computation. Simulation result show that this method can track the mobile user real time and the SNR loss due to estimation error is less than 0.5dB.

矢量信道估计是智能天线技术的一个关键之处.近来,已有人提出了CDMA上行信道的估计算法及递归跟踪算法,但算法仍很复杂、不稳定,且不易用VLSI实现.根据存在问题,我们研究一种非迭代子空间跟踪算法在CDMA移动环境中的性能,包括跟踪的匹配误差、收敛速度、系统容量.上行信道的阵列响应矢量可由接受数据的相关矩阵的主特征矢量来估计,我们将数据相关矩阵分解成一维信号子空间和多维的噪声子空间,并对噪声子空间采用平均的方法,使主特征矢量的更新变成了2×2的特征问题,大大地降低了计算复杂度.计算机仿真结果表明,该方法能实时地跟踪移动速度很快的用户,而由估计误差带来的信噪比损失小于0.5dB

The algorithm of fast subspace tracking based on the novel information criterion(NIC)is extended to principal component analysis (PCA) by means of the deflation technique.Performances of the novel PCA algorithm and the PASTd algorithm are demonstrated and compared through simulation examples.

运用压缩映射技术 ,将一种基于新的信息准则的信号子空间跟踪算法推广为可以直接跟踪信号子空间的特征成分 (各个特征值和对应的特征向量 )的新算法 .并将这种新算法和基于压缩映射的投影逼近子空间跟踪算法的性能用仿真试验进行比较

 
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