A minimum variance distortionless response filter based on the bifrequency spectrum for single-channel noise reduction

This paper deals with the problem of single-channel noise reduction in the short-time Fourier transform (STFT) domain. Many algorithms have been developed to solve this important problem, most of which generally assume that the STFT coefficients in different frequency bands are uncorrelated, so the noise reduction is achieved by applying a gain function to the STFT of the noisy speech in each frequency band. However, this assumption is not accurate and the STFT coefficients of speech signals between neighboring frequency bands are correlated in practice due to the use of small lengths of the fast Fourier transform (FFT) and overlap add/save techniques in implementation. This paper formulates the noise reduction problem by taking into account the interband correlation using the so-called bifrequency spectrum. Based on this formulation, a single-channel minimum variance distortionless response (MVDR) filter is derived, which is shown to be able to significantly improve the signal-to-noise ratio (SNR) and meanwhile maintain the desired speech not much distorted. Simulations are presented to justify the claimed merits of the developed MVDR filter.     

基于近场聚焦波束形成方法的声场定位研究

海洋环境中的声场定位,是水声学研究领域中的一个经典问题,尤其是用于海区警戒来监听敌方潜艇的近场定位问题.以往的近场定位研究,通常使用线列阵模型,利用常规聚焦波束形成方法进行定位处理,不仅出现了“左右舷模糊”问题,而且对信号定位不甚理想.为此提出基于半圆阵的四阶累积量MUSIC近场聚焦波束形成算法.通过仿真比较了多种聚焦波束形成在近场线列阵和圆阵条件下的定位性能,结果表明,半圆阵成功地解决了直线阵列出现的“左右舷模糊”问题,基于四阶累积量的MUSIC近场聚焦波束形成可以达到更为平滑的背景以及较小的旁瓣级,定位性能明显优于基于MVDR算法的聚焦波束形成高分辨率被动定位方法.     

基于Bayesian准则的鲁棒自适应波束形成算法

研究波束形成算法的前提是驾驶向量是确定的.在实际情况下,由于实际的阵列响应和期望信号的阵列响应存在一定的偏差,这时候就需要研究对于驾驶向量不确定情况下的波束形成问题.该文基于Bayesian准则,对一系列自适应MVDR波束形成器的权向量进行后验概率密度加权,得到Bayesian自适应波束形成器.仿真和实际水声阵列数据结果显示,该波束形成器在驾驶向量不确实的情况下,具有较好的鲁棒性.     

一种基于MVDR和CCBC的抗噪语音识别方法

提出了一种适用于抗噪声语音识别的方法，其特征提取过程基于最小方差无失真响应（Minimum variance distortionles sresponse，MVDR）谱估计方法，并对该特征进行频率弯折以提高其知觉分辨率，最后使用基于正则相关分析的谱变换补偿（Canonical correlation based on compensation，CCBC）法对该特征进行自适应处理，从而提高了系统的鲁棒性。在展览馆噪声、人群噪声和汽车噪声下，与基于传统Mel倒谱系数（MFCC）特征的系统进行了对比实验，结果表明使用本文方法的语音识别系统的识别率得到了显著的提高。     

最大稀疏稳健恒定束宽波束形成器设计

为了降低宽带阵列恒定束宽的实现复杂性,在分析宽带阵列稀疏性的基础上,构造了以阵元和抽头延迟线(TDL, tapped delay line)稀疏性的凸组合为目标函数,满足恒定束宽约束的波束形成器优化模型,降低了所需的阵元和TDL个数。引入重加权机制,通过序列凸优化,使稀疏性递增并收敛到最大值,证明了保证波束形成器稳健性的范数约束与最大TDL稀疏目标函数之间的等价性。仿真结果表明,可用较少的阵元及TDL个数获得相同的恒定束宽性能,具有工程实用价值。     

抑制运动强干扰的稳健波束域目标方位估计方法

为了减小来自旁瓣区快速运动的强干扰对波束域高分辨方位估计方法的影响,提出一种稳健的波束域高分辨方位估计方法。该方法在形成多波束时,将稳健自适应波束形成与零陷扩宽技术相结合,有效地抑制了运动强干扰所造成的快拍失效和扫描方向误差引起的自适应波束图畸变,从而保证波束域方法能准确地估计目标方位。仿真结果验证了该方法的有效性。     

Robust optimization of transmit/receive beamformer for MIMO downlink with imperfect CSI at base station

A robust scheme is proposed to jointly optimize transmit/receive beamformers for Multiple Input Multiple Output （MIMO） downlinks where the available Channel State Information （CSI） at Base Station （BS） （CSIBS） is imperfect. The criterion is to minimize the sum Mean Square Error （sum-MSE） over all users under a constraint on the total transmit power, which is a non-convex and non-linear problem. Observing from the first order optimization condition that the optimal transmit/receive beamformers are mutually dependent, the transmit/receive beamformers for each user are updated iteratively until the sum-MSE is minimized. Simulation results indicate that the proposed scheme can effectively mitigate the system performance loss induced by imperfect CSIBS.     

基于相关域的二维自适应波束形成算法

 该文针对使用最小方差无失真响应算法进行2维波束形成时需要大量采样数据和庞大计算复杂度这一问题,提出一种基于相关域的2维自适应波束形成算法。通过将高维权向量分解成两个低维权向量的Kronecker积形式,使用双迭代算法利用相关域信息求解出两个低维权向量,从而降低了估计采样相关矩阵所需的采样数据和计算量。实验仿真表明,所提算法具有良好的收敛性能,在采样数较小的情况下,能够更好地抑制干扰信号。     

基于矢量阵宽带MVDR聚焦波束形成的水下噪声源定位方法

该文研究了可应用于水下噪声源近场定位的矢量阵宽带MVDR聚焦波束形成方法,其中包括基于子频带分解的矢最阵非相干宽带聚焦波束形成(ISMMVDR-VFB)以及矢量阵相干宽带CSS-MVDR聚焦波束形成(CSSMVDR-VFB).两种方法将宽带MVDR高分辨方位估计方法及矢量阵信号处理技术与聚焦波束形成相融合,较常规聚焦波束形成具有更高的分辨力和更低的旁瓣级,能解决"左右舷模糊"问题并提高处理增益.与非相干算法ISMMVDR-VFB相比,CSSMVDR-VFB采用聚焦变换思想,可直接处理相干宽带信号,对于实际水声信号具有更高的适用性.最后通过对研究方法的空间谱仿真分析证明了其正确性和有效性.