基于一定的解码状态，声码器通过最小均方误差（MMSE）估计的方法估计最优参数，充分降低信道误码对重建语音质量的影响。
The minimum mean square error (MMSE) is computed for each decoding state to estimate optimal parameters and to reduce the influence of the bit error.
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.
本文提出了一种改进型语音短时谱最小均方误差(MMSE)估计的增强方法。
This paper proposes a new speech enhancement scheme based on modified Minimum Mean Square Error (MMSE) estimation of speech Short Time Spectrum Amplitude (STSA).
在这种框架中，利用李群来表示旋转空间，然后基于贝叶斯估计结构，计算了利用李群的表示的最小均方误差估计性能界限。
Upon this frame, use matrix Lie group to express the rotation space, then base on Bayesian estimation framework, calculate the minimum mean squared error bounds which use the matrix Lie groups.
本文将分形理论引入多径衰落信道的描述，提出了一种利用分形滤波进行最小均方误差意义下信道参数估计的方法。
The fractal theory was applied to describe multipath fading channels, and a novel channel estimation scheme using fractal filter in the sense of minimum mean square error (MMSE) was presented.
引入了最佳修正短时对数谱估计，能在信号存在的不确定性下最小化语音信号对数谱的均方误差。
We introduce an optimally-modified log spectral amplitude estimator, which minimizes the mean-square error of the log spectra for speech signals under signal presence uncertainty.
该方法可以使估计的总体均方误差最小。
The method focuses on minimizing the ensemble mean square error of the estimation.
基于导频的信道估计方法已经受到广泛的研究，主要估计算法有最小二乘法(LS)、最小均方误差法(MMSE)和最大似然(ML)估计法等。
The channel estimation method based on pilots has received much research. It adopts the algorithms such as least squares (LS), minimum mean square error (MMSE), maximum likelihood (ML), and so on.
本文提出了将RAS-MFCC特征和最小均方误差估计（MMSE）语音增强方法相结合的抗噪声语音识别方法。
At last, we propose a new speech recognition method which combines RAS-MFCCs and MMSE speech enhancement technology.
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.
由于该方案是基于近似的线性最小均方误差估计准则而设计的，因此它是一种理论上的准最佳跟踪方案。
As the scheme is designed conforming to the criteria of approximate linear least-mean-square error estimation, it is theoretically quasi-optimal.
由于该方案是基于近似的线性最小均方误差估计准则而设计的，因此它是一种理论上的准最佳跟踪方案。
As the scheme is designed conforming to the criteria of approximate linear least-mean-square error estimation, it is theoretically quasi-optimal.
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