一种自适应最小范数算法

运用特征子空间方法的关键在于信号子空间或噪声子空间的估计,实际上有些信号的统计特性随时间变化,于是要求得到参数的实时估计值,为此,需要随时根据新的阵列接收数据对信号或噪声子空间进行更新。本文首先分析了一种自适应子空间估计算法,即MALASE(MaximumLikelihoodAdaptiveSubspaceEstimation)算法。然后,把MALASE算法与传统的最小范数(Mini-Norm)高分辨算法相结合,并应用零点跟踪技术,提出了一种自适应Mini-Norm高分辨算法,可用于对时变的信号波达方向(DOA)进行跟踪估计。计算机仿真结果验证了该算法的有效性。     

信噪比后滤波与特征空间融合的最小方差超声成像算法

为了提高超声成像空间分辨率和对比度,提出了一种信噪比后滤波与特征空间融合的最小方差波束形成算法。首先,利用信号子空间划分将最小方差算法得到的权矢量投影到信号子空间中提高成像对比度,然后基于信号相干性设计滤波系数,并引入基于信噪比的噪声加权系数,最终得到融合信噪比后滤波与特征空间的最小方差算法。为验证本算法的有效性,使用FieldⅡ对点目标和吸声斑目标进行了仿真实验验证,并采用密歇根大学geabr_0实验数据进行成像。实验结果表明:所提算法在对比度和分辨率上均有所提高,明显优于传统延时叠加算法,最小方差算法和ESBMV_wiener算法,且对噪声具有较强鲁棒性。      

字典奇异值分解加权压缩感知多径信号参数估计

为提高水声信道多径参数估计的分辨率,提出了一种基于字典奇异值分解的加权压缩感知算法。对于有源声呐,根据发射信号构造字典,对字典进行奇异值分解,利用大特征值对应的特征向量构造信号子空间,然后使用信号子空间对接收信号进行滤波。对滤波结果进行加权压缩感知参数估计,得出最终时延估计结果。仿真实验表明,所提方法能够对水声多径参数进行超分辨估计,适用于任何脉冲信号。湖试处理结果显示,混响背景下该方法也有较好的多径参数估计性能,能够降低接收数据的噪声成分,提高对水声信道的多径时延、个数和幅度的估计精度。      

基于高斯-拉普拉斯-伽玛模型和人耳听觉掩蔽效应的信号子空间语音增强算法

针对信号子空间语音增强算法中的子空间选择和线性滤波器中噪声功率谱和拉格朗日乘子的估计问题,用高斯、拉普拉斯和伽玛模型描述了语音的分布,提出了利用目标语音概率最大化来确定信号子空间维度的方法。在噪声子空间上,利用条件概率估计出噪声功率谱。接着,为了合理地折中增强语音中的残余噪声和语音畸变,提出了一种基于人耳听觉掩蔽效应的拉格朗日乘子估计方法。实验证明,在多项语音质量评价指标上,所提算法都取得了更好的结果。所提的信号子空间算法比传统的信号子空间算法更有效地消除了噪声,使得恢复的语音具有更好的质量。      

低复杂度的MIMO声呐协方差矩阵重构方法*

多输入多输出声纳在对目标进行测向时会产生复杂的运算量,从而降低算法的测向效率。针对这一问题,提出了一种基于降维变换方法的低复杂度协方差矩阵重构方法。该方法能够抑制噪声,提高目标侧向性能。首先利用降维变换方法对接收信号进行波束形成,获得低维度的协方差矩阵,再对矩阵进行Toeplitz处理,抑制矩阵的相干性。所得到的新的协方差矩阵,通过特征分解获得噪声子空间和信号子空间,利用MUSIC方法进行测向。为了进一步降低运算复杂度,利用阵型所满足的旋转不变性,可以采用ESPRIT算法对目标进行波达方向估计。理论分析和实验结果表明,该方法有效降低了运算复杂度,提高了算法的测向性能。在有限快拍数的情况下,与传统测向方法相比,具有运算速度快,目标分辨力强的特点。     

A subspace approach based on embedded prewhitening for voice activity detection

This paper presents a subspace approach for voice activity detection (VAD). The proposed approach is based on an embedded prewhitening scheme for the simultaneous diagonalization of the clean speech and noise covariance matrices to provide a decision rule based on likelihood ratio test in signal subspace domain. Experimental results show that the proposed subspace-based VAD algorithm outperforms the method using a Gaussian model in a conventional discrete Fourier transform domain at the low signal-to-noise conditions.     

基于信号子空间增强和端点检测的大地电磁噪声压制

为了进一步保留大地电磁低频段的有用信息、提高矿集区复杂噪声环境下大地电磁测深深部探测能力,在形态滤波的基础上结合信号子空间增强和端点检测做二次信噪分离处理.首先,针对形态滤波预提取的噪声轮廓运用信号子空间增强分离出信号子空间和噪声子空间.然后,将信号子空间和重构信号相结合并将噪声子空间置零.最后,借鉴端点检测做后处理,以识别波形突变的起止点.仿真结果表明,卡尼亚电阻率曲线在低频段的数据质量得到了明显改善、视电阻率值相对稳定;有效地补偿了形态滤波处理过程中损失的低频有用信号,其结果更加真实地反映了测点本身所固有的大地电磁深部构造信息.     

波达方向估计中特征空间的信源数估计方法

提出了特征空间法信源数估计方法,它将阵列信号的协方差估计值分别投影到信号的特征子空间和噪声的特征子空间。由于信号子空间与噪声子空间相互正交,易于由表征投影大小的判据值区分信号和噪声的贡献;本方法用的是M×M阶矩阵特征值分解,M为基元数,与波达方向估计用的相同,因此节省大量的计算量;它可以在实数空间中进行运算,进一步减少运算量。进行了数值计算,检验了判据值分布,以及在信源等功率、不等功率和空间相关色噪声等情况下特征空间法的性能。估计方法还用声纳数据进行了检验。所有这些结果均证明本估计方法性能优良。      

A signal subspace dimension estimator based on F-norm with application to subspace-based multi-channel speech enhancement

Although the signal subspace approach has been studied extensively for speech enhancement,no good solution has been found to identify signal subspace dimension in multichannel situation.This paper presents a signal subspace dimension estimator based on F-norm of correlation matrix,with which subspace-based multi-channel speech enhancement is robust to adverse acoustic environments such as room reverberation and low input signal to noise ratio (SNR).Experiments demonstrate the presented method leads to more noise reduction and less speech distortion comparing with traditional methods.     

一种基于选择性测量的自适应压缩感知方法

针对低信噪比条件下现有压缩感知系统重构性能严重恶化的问题,提出了一种基于选择性测量的自适应压缩感知结构.首先推导并分析了经过压缩测量的噪声的统计特性及其对重构性能的影响;然后基于输出能量最小化准则,设计了一种压缩域投影滤波联合噪声检测的自适应感知器,感知获得噪声子空间的位置信息;进一步利用该信息构造选择性压缩测量矩阵,智能选择测量信号,同时"屏蔽"噪声分量,极大提高了压缩测量值的信噪比.仿真结果表明,相对于现有压缩感知结构,选择性测量的压缩感知结构明显改善了含噪稀疏信号的重构性能,可更好地应用于吸波材料的前端特性分析、认知无线电的频谱感知等领域.     

一种能量比调控的有源噪声控制算法*

在前馈有源噪声控制系统中,建模信号与控制信号相互影响,建模信号的引入会导致系统降噪性能变差。为了减小建模信号的影响,提出一种基于能量比调控的次级通道在线建模有源噪声控制算法。利用控制过程与建模过程的误差能量比构造步长调控函数,分别调节控制过程与建模过程的步长值,从而减弱两者的相互影响。在次级通道建模过程中,对建模步长值采取分段调控的方法,并通过建模步长值的变化来调节建模信号,从而提升系统降噪性能。仿真结果表明,对于低频噪声信号的有源噪声控制,相比已有算法,提出的算法能获得较快的建模收敛速度和较高的降噪量。     

An adaptive way for improving noise reduction using local geometric projection

We propose an adaptive way to improve noise reduction by local geometric projection. From the neighborhood of each candidate point in phase space, we identify the best subspace that the point will be orthogonally projected to. The signal subspace is formed by the most significant eigendirections of the neighborhood, while the less significant ones define the noise subspace. We provide a simple criterion to separate the most significant eigendirections from the less significant ones. This criterion is based on the maximum logarithmic difference between the neighborhood eigendirection lengths, and the assumption that there is at least one eigendirection that corresponds to the noise subspace. In this way, we take into account the special characteristics of each neighborhood and introduce a more successful noise reduction technique. Results are presented for a chaotic time series of the Henon map and Ikeda map, as well as on the Nasdaq Composite index.     

Spatial time-frequency DOA estimation based on joint diagonalization using Jacobi rotation

Direction-of-arrival (DOA) estimation consists of locating closely spaced sources impinging from different directions in the presence of considerable noise or interference. Recently, this problem has been solved using spatial time-frequency distribution (STFD) information that is available in the array signals. In this work, a new joint diagonalization approach based on Jacobi rotation, which can efficiently combine all of the relevant STFD points, is proposed to achieve superior DOA resolutions and suppressed sidelobes in the spatial spectrum. It is discovered that our proposed Jacobi technique leads to a non-orthogonal joint diagonalization structure and can avoid pre-whitening the signal component of the observation. In addition, we further adopt the minimum variance distortionless response (MVDR) processor instead of the MUltiple SIgnal Classification (MUSIC) algorithm to avoid the estimation of the signal subspace and noise subspace in the time-frequency DOA domain. Finally, computer simulations of several frequently encountered types of challenging scenarios (such as low SNR and coherent arrivals) show that significant improvements are achieved by our proposed approach in comparison to the existing techniques.     

基于信号子空间重构的鲁棒子区域Frost波束形成

本文针对一个感兴趣子区域内的宽带波束形成问题, 提出一种基于信号子空间重构的鲁棒Frost波束形成算法. 该算法的基本思想是利用一个矩阵滤波器从所估计的信号加干扰子空间中提取感兴趣信号(SOI)的特征分量; 然后利用该特征分量重构信号子空间; 最后利用重构的信号子空间构造一组线性约束最小方差(LCMV)准则, 来保证SOI近似无失真通过. 与现有的其他鲁棒Frost波束形成算法相比, 本文算法的一个显著优点是在未知SOI实际波达方向与频带等先验信息的情况下, 其导向与带宽均能够自适应地匹配SOI. 因此在整个感兴趣子区域内, 它都能获得接近最优值的输出信干噪比. 理论分析与仿真研究验证了算法的有效性.     

应用半正定规划的目标方位超分辨方法

针对水下目标方位超分辨估计问题,提出了一种基于半正定规划(Sdp)的常规波束(CBF)方位超分辨算法(SdpCBF).Sdp-CBF算法基于常规波束形成获得多目标方位谱数据,利用阵列响应矩阵和半正定规划技术,精确估计目标数量和波达角方向.该算法的本质是利用阵列特性和信号能量信息获得超分辨方位估计,不用进行子空间分解,通过卷积反演的方式将阵列孔径的有限效应消除,在L₂范数约束条件下重构空间谱.仿真表明,Sdp-CBF算法具有较强的噪声抑制能力,对非相干和相干信号均具有目标方位超分辨能力,在低信噪比环境下的方位分辨性能超过多重信号分类(MUSIC)等经典高分辨算法。对消声水池以及湖上实验数据的处理结果显示,Sdp-CBF算法在复杂环境中对相干信号及微弱信号具有较强的分辨能力。      

稀疏低秩噪声模型下无监督实时单通道语音增强算法

针对现有基于字典学习的增强算法需要先验信息、不易实时处理的问题,提出一种便于实时处理的无监督的单通道语音增强算法。首先,该算法将无监督条件下背景噪声的建模问题转化为带噪语音幅度谱的稀疏低秩噪声分解;然后,采用增量非负子空间方法对背景噪声进行在线字典学习,获得能够体现背景噪声时变特性的自适应噪声字典;最后,利用所得的噪声字典,采用易于实时处理的逐帧迭代方式,对带噪语音进行处理。实验结果表明:相较于多带谱减法和基于低秩稀疏矩阵分解的增强算法,所提算法在噪声抑制方面的性能尤为显著,在多项性能评价指标上,均表现出更好的结果。      

A wavelet-based forward BSS algorithm for acoustic noise reduction and speech enhancement

In this paper, we address the problem of noise reduction and speech enhancement by adaptive filtering algorithm. Recently, the well known forward blind source separation (FBSS) structure has been largely studied and intensively used to reduce acoustic noise components and to enhance speech signal. The FBSS structure is often combined with adaptive algorithms to accelerate the adaptation of the cross-filters, and to improve noise suppression at the output. In this paper, we propose to use a wavelet transform decomposition in the FBSS structure by using a two-channel forward wavelet symmetric adaptive decorrelating (WFSAD) algorithm. The proposed WFSAD algorithm provides a better compromise between time and frequency resolution and improves robustness of the noise reduction process when compared with the classical two-channel forward symmetric adaptive decorrelating (FSAD) algorithm. Simulation results prove the efficiency of the proposed WFBSS algorithm in comparison with conventional ones in terms of several objective and subjective criteria.     

密布式多输入多输出声呐阵列目标波达方向估计

针对低信噪比条件下多输入多输出声呐受对称噪声分量影响导致测向性能降低的情况,提出了一种基于协方差矩阵重构方法的波达方向估计算法。首先,将噪声场分为对称噪声和非对称噪声两部分,利用协方差矩阵虚部与对称信号无关的性质,去掉协方差矩阵的实部来降低对称噪声对目标波达方向估计精度的影响,采用降维转换方法和矩阵虚部置换原理重构协方差矩阵的实部,避免了双频谱的干扰。然后利用Toeplitz方法对重构的协方差矩阵进行解相干修正,通过奇异值分解获得噪声子空间,最后对目标的波达方向进行估计,可实现微弱信号的准确测向。理论分析和实验结果表明,该方法明显抑制了对称噪声,提高了目标的波达方向估计性能,具有运算速度快、自由度高和目标分辨力强的特点。      

一种在色噪声环境下的信号检测方法

为了抑制蛟龙号载人潜水器水声通信受母船色噪声干扰的影响,本文提出了一种统计匹配滤波器在色噪声环境下使用的新方法,它通过将接收信号映射到一个生成子空间中进行信号检测,可提高输出水声通信信号的信噪比。受客观条件限制,向阳红09船——蛟龙号载人潜水器的试验母船噪声环境比较恶劣,对水声通信产生了较强的干扰。如何在向阳红09船的这种强、色噪声环境下,提升水声同步信号的检测能力,对提高水声通信的可靠性是非常有帮助的。实际采集试验数据分析结果验证了该检测算法比传统匹配滤波器有更强的信号检测能力。     

Active control of transformer noise with an internally synthesized reference signal

A method of using an internally synthesized signal of preset frequencies as the reference signal for transformer noise control is proposed in this paper. The feasibility of applying the method in a specific transformer algorithm is investigated, where the effects of frequency mismatch and secondary path delay on the steady-state behavior of the transformer algorithm are analyzed and compared with the filtered-x least mean square algorithm. It is found that the noise reduction performance degrades with the increase of secondary path delay and frequency mismatch, but can be improved by using a larger convergence step size and a faster update rate. With parameter studies, the feasibility of applying the transformer algorithm with an internally synthesized reference signal to practical transformer noise control is verified by both numerical simulations and experiments.