LMS Blind Space-Time Multiuser Detection in Multipath CDMA Channels;
多径CDMA信道下最小均方盲空时多用户检测
This novel adaptive beamforming algorithm uses FFT for the received signal,passes the band-pass filter and finally uses the LMS algorithm to implement adaptive.
该波束形成算法先对输入信号进行FFT,然后在空间频域上带通滤波,最后通过LMS(最小均方)算法实现自适应波束形成。
This paper proposes a LMS filtering method for channel estimation in OFDM systems.
提出了一种适用于OFDM系统的最小均方(LMS)滤波的信道估计算法,对发送序列中导频位置的信道响应进行LMS滤波,进一步得出所有子载波上的信道响应。
The transformed signal is used as the input to least mean square self-adapting filter algorithm with stage Ⅱ.
第二级将变换后的输入信号用做最小均方自适应滤波算法的输入。
The least mean square (LMS) linear prediction method was introduced to detect data points of a surface.
利用最小均方 (LMS)线性预测方法检测曲面型值点 ,对误差较大的坏点采用预测值进行修正 ,从而有效地避免了坏点对曲面光顺效果的影响 。
The popular TDE algorithms mainly include the classical generalized cross correlation(GCC),the adaptive least mean square(LMS),the subspace based eigenvalue decomposition(EVD)and the acoustic transfer functions ratio(ATF-s ratio) methods,et.
现有的TDE算法主要包括经典的广义互相关(GCC)方法、自适应最小均方(LMS)方法、基于子空间的特征值分解(EVD)方法和基于传递函数比(ATF-s ratio)的方法等。
The Turbo equalization with MMSE soft-cancellation can eliminate co-channel interference(CCI) and multiuser interference, which is an important technique for frequency selective fading channel and need obtain filter coefficients by minimum-mean-squared-error (MMSE) for multi-antenna transmit systems, but there are different coefficients with different constraints with space and time variable.
基于最小均方差(MMSE)的软干扰消除Turbo均衡器能有效地消除系统共信道干扰与多用户干扰,在频率选择信道下的是一种非常有效的均衡接收技术。
typical detection methods, including ML and MAP of non-linear detection technology, and QR, ZF and MMSE of linear detection technology.
论文采用了多种空时信号处理技术,如典型的检测方法,包括非线性检测技术的最大似然检测技术(ML)和最大后验概率检测技术(MAP),以及线性检测技术的QR分解算法、迫零(ZF)算法和最小均方差(MMSE)算法,重点采用了高性能多天线检测技术—EM算法,介绍了它的原理、性质以及基于V-BLAST系统的应用。
The state equation and measurement equation for detecting the weld position is established,and the optimal estimation of the Kalman filtering recursive algorithm also is established according to the principle of minimum mean square error.
以焊缝中心位置为特征矢量,建立焊缝位置检测的状态方程和测量方程,并依据最小均方差原则建立了卡尔曼滤波最优估计的递推算法。
Channel equalization of orthogonal frequency division multi-plexing underwater communication based on linear minimum mean square error
OFDM水声通信线性最小均方误差算法信道均衡
linear-minimum-mean-square-error estimator
线性最小均方误差估计量
The Improved Least Mean Square Algorithm of Smart Antenna Research;
智能天线中改进最小均方算法的研究
DOA Passive Location Algorithm on Least Mean Square Error Criterion
一种基于最小均方误差准则的唯方位定位方法
An Amplitude-only Direct Data Domain Least Square Algorithm of Wideband Signals Based on the Uniform Circular Array
基于均匀圆阵的宽带信号唯幅度直接数据域最小均方算法
Three Dimensional Adaptive Prediction of Video Coding Based on LMS
一种基于最小均方差的3D自适应预测视频编码
Image denoising of coal dust based on improved minimum mean square error estimation
改进最小均方误差估计的煤尘图像去噪
Implementation of Adaptive Filter on GPS Receiver Based on LMS Algorithm
基于最小均方误差算法消除GPS接收机噪声
Noise suppression MOE blind multiuser detection based on constrained least mean square
基于约束最小均方的噪声抑制MOE盲多用户检测
Linear precoding based on MMSE in multi-user MIMO downlink
基于最小均方误差的多用户MIMO下行预编码
Linear Optimal Filter with Minimum Mean Square Error for Synthetic Aperture Radar Images
合成孔径雷达图像的最小均方误差线性最优滤波
Abstract: We present a method of circular arc fragmented curve-fittingbased on least mean-square error rule in this paper.
文摘:提出了一种基于最小均方误差准则的圆弧分段曲线拟合方法。
Radio Resource Allocation Based on Mmse Multiuser Receiver;
基于线性最小均方误差多用户接收机资源分配的研究
A Channel Estimate Algorithm Based on Least-Mean-Square Error in Wireless LAN;
一种基于最小均方误差的无线局域网信道估计算法
Implementing Noise Cancellation of 10 kV Power Line Communication Based on Adaptive Least-Mean-Square Error Algorithm
基于自适应最小均方误差算法实现10kV电力线通信的噪声对消
Adaptive time delay estimation based on least mean square Sigmoid error with the presence of impulsive noises
α稳定噪声下最小均方Sigmoid误差自适应时间延迟估计
The method focuses on minimizing the ensemble mean square error of the estimation.
该方法可以估计的总体均方误差最小。
Mean-variance Hedging under Stochastic Interests;
随机利率下的均值-方差最小套期保值
CopyRight © 2020-2024 优校网[www.youxiaow.com]版权所有 All Rights Reserved. ICP备案号:浙ICP备2024058711号