A modified MMSE algorithm for speech enhancement based on speech periodic property is presented.
基于语音周期性的特点，提出了一种预加重的MMSE语音增强的改进算法。
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。
About denoising algorithm this paper researches focused on Spectral Subtraction algorithm and improved form, STSA-MMSE algorithm, using MATLAB tool to simulate these algorithms.
在去噪算法上本文重点研究了谱减法及其改进形式、STSA - MMSE去噪算法，用MATLAB工具仿真了这些算法。
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.
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.
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。
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