提出一种基于循环最小均方算法的自适应波束形成算法。
Firstly, propose a novel adaptive beamforming algorithm based on cyclic least mean square (CLMS) algorithm.
经过频率粗同步后，以变步长的最小均方算法(VLMS)进行频率补偿。
After coarse frequency synchronization, frequency offset compensation is carried out using variable step size least mean square algorithm (VLMS).
最小均方算法（LMS）以计算简单，易于实现等优点被广泛用于自适应滤波领域。
The least mean square (LMS) algorithm is abroad applied in the field of adaptive filter dealing with simply computation and easy realized.
在模型的训练过程中运用了最小均方算法，其优点是收敛速度很快，可以用于实验室数据处理。
Least mean squares (LMS) algorithm was used in the training procedures. The merit of the algorithm is the fast convergence and it can be used for data analysis in laboratories.
本文根据现有的步长调整原则，利用误差和步长关系曲线特点，提出了新的变步长最小均方算法。
The paper put forward a new improved least mean square algorithms which is on the basis of the fundamental of convergence step and the graph connection between error and step.
本文指出最小均方算法并不能使均方误差最小，证明了最小均方算法实际上是一种加权最小二乘算法。
It is shown in this paper that the Least-Mean-Square algorithm cannot minimize the mean square error, and that it is actually a kind of weighted Least-Square algorithm.
着重介绍采用最小均方算法的横向滤波器、判决反馈均衡器及盲均衡器，讨论了HDTV中采用的不同均衡方案及其均衡性能。
The emphasis is laid on the principles of LMS transversal filter, decision feedback equalizer and blind equalizer. Different equalizing schemes and their performance used in HDTV are discussed.
仿真结果表明最小均方自适应算法、归一化最小均方算法、基于QR分解的平方根信息自适应滤波器和基于QR分解的最小二乘格型自适应滤波算法都能有效地滤除海浪噪声。
It can be drawn from the simulation of the sea adaptive filter adopting various algorithms mentioned that the LMS, NLMS, QRRLS and QRDLSL are all effective to denoise the sea noise.
本文给出了在混合噪声中非线性递归最小均方误差算法的性能分析。
This paper presents the performance analysis of recursive least square algorithm with error-saturation in mixture noise.
利用基于滤波X最小均方值算法的前馈型自适应控制系统，对汽车座椅振动实施主动控制，以改善座椅的动态舒适性。
An adaptive controller which contains a filtered-x least mean squares algorithm(FXLMS) was applied to complete the active vibration control of a vehicle seat.
在建立回波抵消器模型的基础上，按最小均方误差准则，导出了自适应滤波器抽头的统计梯度算法和抽头调节的收敛公式。
Based on building up a model of echo canceller, the convergence of gradient-type stochastic adjustment algorithm of an adaptive filter under the mean-squared error criterion is discussed.
该算法实现了横摆角速度的线性最小均方误差估计，且可对汽车行驶过程中的系统噪声和观测噪声统计特性进行在线估计。
This algorithm can realize linear minimum mean square error estimation of yaw rate, and on-line estimate statistical characteristic of system noise and observation noise during vehicle running.
本文介绍了自适应滤波器及其最小均方(LMS)算法，通过计算机进行了仿真实验并对仿真结果进行了讨论。
The adaptive filter and its least mean square (LMS) algorithm are reviewed. Computer simulation tests were made and results obtained therefrom are discussed.
该技术基于次优化子空间盲最小均方误差（MMSE）多用户检测算法。
This technique is based on the suboptimal subspace blind minimum mean square error (MMSE) algorithm of multiuser detection.
给出了基于差分最小均方误差(DMMSE)准则的短扩频序列ds- CD MA自适应多用户检测算法。
Algorithms based on DMMSE (Difference minimum mean square error) criterion of adaptive multi users detector for short spreading sequences DS CDMA systems is proposed.
提出一种改进的归一化最小均方(MNLMS)算法，并用该算法驱动FIR滤波器以实现对象模型及其逆的辨识。
A modified NLMS (MNLMS) algorithm was proposed and applied to drive a FIR filter to approximate both the model and the inverse of plants.
本文讨论了一种带有参考通道的自适应环境噪声滤波器原理，该滤波器采用最小均方误差（LMS）算法。
One kind of theories of adaptive noise cancellation filter with referent channel based on Least Mean Square (LMS) algorism is discussed in this paper.
采用常用的最小均方误差(LMS)自适应算法，研究了自适应解调方法对A SK信号的解调及其性能。
The demodulation and performance of adaptive demodulation method to ASK signals was studied by Least Mean Squares algorithm (LMS) in common use.
该算法采用协方差匹配技术，依据滤波新息，动态调整测量噪声方差，使融合系统的均方误差始终最小。
With covariance matching technique and innovation information of filtering, the noise variance is dynamically adjusted and the mean square error of the fusion system always keeps minimum.
仿真实验显示，本章提出的五种检测器算法的误码率均远低于已有的解相关、最小均方误差和多级多用户检测器。
Our simulation experiments show that the bit-error-ratio (BER) of these new detectors is much lower than that of decorrelation, minimum mean-squared error and multistage detectors.
为了滤除这些不平衡信号，通过分析几类最小均方(LMS)误差算法，得出一种相对合适的滤波算法。
To filter the unbalance signals, one kind of relatively suitable filter algorithm was proposed through analyzing some kinds of least mean square (LMS) algorithms.
NLMS算法是回声消除器中最常用的算法之一，然而语音信号的强相关性使NLMS(归一化最小均方)算法的收敛速度变慢。
NLMS (normalized least mean square) algorithm is widely used in echo canceller. However, due to high correlation of speech signals, the performance of echo canceller based on NLMS is depressed.
提出均方误差最小意义上的最佳三维子带码率分配算法，该算法在任意给定码率限制条件下都能获得最优率失真性能。
An optimal subband rate allocation algorithm in mean-squared error (MSE) minimization sense was proposed, which achieves persistent optimal rate-distortion performance under any given rate constrains.
针对快跳频多址系统，提出了基于最小均方误差（MMSE）准则的多用户检测算法。
This technique is based on the suboptimal subspace blind minimum mean square error (MMSE) algorithm of multiuser detection.
该技术基于次优化子空间盲最小均方误差(MMSE)多用户检测算法。
This technique is based on the suboptimal subspace blind minimum mean square error (MMSE) algorithm of multiuser detection.
此算法相对于均方误差最小准则和最大误差最小化准则具有占用存储空间小和计算时间短的优点。
Compared with the minimum mean squared error principle and the minimaxs principle this one needs smaller memory space and less computation time.
自适应滤波算法采用变步长最小均方误差算法，减少了算法的运算量，提高了算法的收敛速度。
The adaptive filter is based on variable step size LMS method (VSSLMS), which can reduce the computation requirement and improve the convergence speed.
对LMS（最小均方）自适应滤波算法进行了初步研究，并将其应用于此振动主动控制系统中。
The least mean square (LMS) algorithm is researched primarily and applied to the vibration active control system.
通过和最小均方误差估计算法(MMSE)相比较，变门限聚类定位算法可有效消除低质量数据对定位结果的影响，从而提高了目标的定位精度。
By eliminating the possible influence produced by low quality data, the presented algorithm can improve localization precision effectively in comparison with the MMSE algorithm.
通过和最小均方误差估计算法(MMSE)相比较，变门限聚类定位算法可有效消除低质量数据对定位结果的影响，从而提高了目标的定位精度。
By eliminating the possible influence produced by low quality data, the presented algorithm can improve localization precision effectively in comparison with the MMSE algorithm.
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