An improved TOA estimation algorithm based on denoised MVDR for B5G positioning

This paper proposes an improved minimum variance distortionless response (MVDR) based TOA estimation algorithm for 5G NR signals under multipath environments. The proposed algorithm achieves high resolution by exploiting a large number of subcarriers of 5G signals and reduces the dimension of the covariance matrix involved in MVDR substantially by utilizing a novel smoothing scheme. Since MVDR requires a relatively high signal-to-noise ratio (SNR), a denoising method is used to improve the TOA estimation performance. Simulation results show that the proposed algorithm achieves much higher resolution than the Bartlett beamformer (BF) and the TOA estimation accuracy remains high over a wide range of SNRs.     

Beamforming using spatial matched filtering with annular arrays (L)

A linear array beamforming method for ultrasonic B-mode imaging using spatial matched filtering (SMF) and a rectangular aperture geometry was recently proposed Kim et al., [J. Acoust. Soc. Am. 120, 852-861 (2006)]. This letter extends those results to include circularly symmetric apertures. SMF applied to annular arrays can improve the lateral resolution and echo signal-to-noise ratio as compared with conventional dynamic-receive delay-sum beamforming. At high frequencies, where delay and sum beamforming is problematic, SMF showed greatly improved target contrast over an extended field of view.     

有限采样下低频声源聚焦波束形成的定位研究

依赖于基阵协方差矩阵的算法,要求有足够样本容量来实现稳健的性能。针对常规聚焦波束形成在低频段分辨能力不足以及有限的数据长度,本文研究了一种基于最小方差无失真响应(MVDR)的重叠分子阵算法。仿真实验表明,重叠分子阵MVDR算法在低快拍数、低信噪比情况下,稳健性优于全阵列MVDR算法。在海试数据中,利用一条孔径为109m的光纤水听器阵列,验证了该算法可以在低快拍数下对50m内低频段100～200Hz的目标实现分辨率为0.5m的近场定位。     

波束形成与独立分量分析融合的宽带高分辨方位估计方法

针对波束域高分辨方位估计存在方位估计偏差大等缺点,提出了将波束形成与独立分量分析融合的宽带高分辨方位估计方法:(1)阵元域数据转换到波束域后,对多波束数据利用基于二阶统计量的独立分量分析算法处理,(2)将波束域独立分量分析融合方法输出结果重构回阵元域信号,(3)对重构后的阵元域数据,使用约束最优化方法改进的MUSIC算法做高分辨方位估计。最后利用ICS (Interactre Circuts&System)声呐模拟器数据和海试数据对算法进行了验证。结果表明:本文方法在弱目标检测、观测区域外强干扰抑制、方位分辨率方面都优先于波束域MUSIC和波束MVDR方法。      

基于任意阵的最小方差无失真响应聚焦波束形成的被动定位方法

将最小方差无失真响应(MVDR)聚焦波束形成近场被动定位方法应用到平面任意结构阵列上,可以很好地解决线列阵由于阵长较长,在实际使用中存在布放困难、部分阵元失效影响定位性能等问题。本文首先分析了平面任意结构阵列的近场接收信号模型,推导出阵列流型,然后将MVDR聚焦波束形成技术应用到该模型中,得到了基于任意阵的MVDR聚焦波束形成近场被动定位方法。仿真验证了该方法的可行性和有效性。     

声矢量圆阵宽带相干目标MVDR方位估计*

针对宽带相干目标的远程探测问题，本文提出一种基于声压振速联合处理和矢量重构的声矢量圆阵MVDR波束形成方法。该方法利用相位模态变换技术，将声矢量圆阵变换为与信号频率无关的虚拟线阵，并构建虚拟线阵声压与组合振速的互协方差矩阵，利用声压与振速各分量间的空间相关性有效地抑制各向同性环境噪声；并对宽带相干信号的互协方差矩阵进行矢量重构，即将最大特征值对应的特征向量划分为相互重叠的子向量，从而构建前/后向Hermitian矩阵；最后，基于MVDR波束形成器实现宽带相干目标的方位估计。仿真计算和实验数据处理结果表明，该方法具较强的解相干能力和噪声抑制能力以及较高的方位估计性能。     

快速收敛最小方差无畸变响应算法研究及应用

常规最小方差无畸变响应(MVDR)自适应波束形成是一种高分辨窄带波束形成器,它是利用实际声场的窄带互谱密度矩阵(CSDM)估计出自适应波束形成权向量。在实际应用中,MVDR算法需要较长的观测时间估计协方差矩阵,不利于对高速运动目标进行定位;对于宽带目标信号,MVDR算法需要对每一个CSDM进行求逆运算,计算量较大;在相干源条件下,目标信号之间会发生"对消"现象,MVDR算法性能急剧恶化。本文提出了基于子带子阵处理的快速收敛MVDR自适应波束形成方法。首先将全频带划分成一组子带,将接收线阵划分成一组子阵,然后对每一子带计算降维的驾驶协方差矩阵(STCM),从而得到快速收敛MVDR自适应波束形成的权值和空间谱估计结果。同时采用双向空间平滑方法对相干源进行MVDR空间谱估计。仿真和海试数据处理结果表明该算法在保证高分辨力的同时,具有瞬时收敛的性能,双向空间平滑技术具有良好的解相干性能。      

A weighting orthogonal method for constant beamwidth beamforming matrices

A weighting orthogonal method for constant beamwidth beamforming matrices is proposed. This method multiplies weighting factors to each orthogonal beamforming matrix corresponding to different frequency bins. The method proposed doesn't cause waveform aberration, and doesn't cause additional loss of array signal-to-noise ratio when the sources have uniform spectrum. The waveform aberration and additional loss of array signal-to-noise ratio can not be avoided simultaneously by ordinary orthogonal method. So we can get good detection and estimation performances at the same time by the weightmg method. Simulation results and water tank experiments are presented to confirm the conclusion above.     

Array gain of fourth-order cumulants beamforming under typical probability density background

The fourth-order cumulant of zero mean Gaussian distribution noise always equals to zero theoretically.In practice the probability density of noise and reverberation is the key problem to performance of the fourth-order cumulant beamforming technique.In this paper,the array gain functions of the fourth-order cumulant beamforming are deducted considering the instantaneous amplitude distribution of the ambient sea noise and bottom reverberation respectively.And the relationships are determined between array gain and the factors including the number of the array elements,the fourth-order and second-order statistical properties of the noise and reverberation,and the input signal-to-noise ratio.It is also verified that there is a critical signal-to-interference ratio and the fourth-order cumulant beamforming can obtain higher gain and resolution than the conventional beamforming method when the ratio is larger than it.The results of experiment data processing demonstrate that the gain and the resolution of the fourth-order cumulant beamforming coincide with the theoretic.     

一种深度学习的立体阵波达方向估计方法

针对常规波束形成主瓣宽且目标分辨能力低的问题,提出一种基于深度卷积神经网络的波达方向估计方法。算法使用常规波束形成计算二维空间功率谱,将预处理后的空间功率谱图输入深度卷积神经网络。该文利用神经网络学习解卷积映射关系,输出主瓣宽度更窄的空间功率谱图,从而实现高分辨率二维波达方向估计。该算法对阵列结构没有限制,适用于立体阵。仿真结果表明该文方法在不同目标个数、快拍数及信噪比参数下均能准确估计目标方向。该文方法目标分辨能力优于常规波束形成方法。在低快拍情况下,目标方向估计误差低于自适应波束形成方法。     

双面声阵列波束形成的正则化改进算法

双面声阵列波束形成能够区分识别位于不同扫描平面的相干声源,然而该算法在低信噪比条件下识别精度较低。针对此问题提出一种迭代正则化改进算法,通过迭代方法更新正则化矩阵与波束形成输出,在不断提升正则化稳定性、抑制干扰旁瓣的基础上使声学云图主瓣向实际相干声源点处聚焦。数值仿真与实验算例结果显示,改进算法在中高频代表频率下能够正确区分相干声源前后方位,并具有相对原算法更高的识别精度。从而表明:从反问题正则化角度对原算法进行优化改进是理论可行的;正则化矩阵的具体形式与广义逆波束形成输出的空间分辨率紧密相关,且可通过迭代方法将二者整合以提高声源识别精度。      

基于时反算子分解的时反高分辨率定位技术研究

基于时反算子分解技术,提出了主动时间反转选择性定位方法。为提高此定位方法的分辨率,根据时反算子和协方差矩阵的相似性,提出了时反MUSIC和对角线加载时反MVDR定位技术。传统的获取时反算子的方法是通过单个阵元依次发射,该方法的缺点是每次只有单个元发射,导致输入信噪比较小,本文提出通过多个波束正交发射获取时反算子,能得到较大的输入信噪比,从而改善三种定位方法的定位性能。波导实验分别对三种定位方法和正交发射技术进行了验证,结果显示三种定位算法中,子空间基于的时反MUSIC方法具有较好的定位精度,时反MVDR技术具有较高的分辨率。      

拖线阵机动时的自适应波束形成

提出了一种MVDR(最小方差无失真响应)的改进算法,用以解决常规MVDR算法由于阵形时变而出现的性能下降问题。在获得时变阵形估计数据的基础上,该算法以统计时段内的平均阵形为基准阵形,在每个扫描方向上根据实际阵形和基准阵形的差异对阵列互谱矩阵多样本进行相位补偿,从而实现统计时段内的互谱矩阵多样本相干累加和目标检测。数值仿真与海上实验数据处理结果表明:与传统MVDR算法相比,改进算法有效缓解了时变阵形下的目标测向角度模糊问题,可提高拖线阵目标左右舷分辨性能、增强弱目标检测能力。      

高增益对角减载波束形成方法研究

实际的海洋噪声包括不相关噪声成分和相关噪声成分,针对不相关噪声仅仅影响接收数据协方差矩阵对角元素的这一现象,提出一种基于对角减载技术的高增益最小方差无畸变响应法(minimum variance distortionless response,MVDR).在稳健性足够的前提下,首先,按照一定准则选取约束量,利用加权向量范数约束法保证方法的稳健性。然后,利用牛顿迭代法计算得到对角减载系数。最后,将减去对角减载系数的协方差矩阵应用到MVDR波束形成方法中。仿真和海试数据处理结果表明,对角减载MVDR波束形成方法在保证稳健性的同时,提高了阵增益和多目标分辨能力。通过调整减载系数,能够在稳健性与阵增益之间进行折中。      

平面波超声成像中的波束合成方法研究进展*

平面波成像通过单次全孔径发射-接收即可获取整幅图像,将成像帧频显著地提升至1000帧/秒以上。然而,平面波成像过程中发射的非聚焦波束将导致回波信号信噪比降低,进而使图像的分辨率和对比度变差。通过多角度相干复合成像技术可以改善平面波成像的图像质量,但是会以牺牲帧频为代价。因此研究人员们开始将新型波束合成技术引入平面波成像中,例如自适应波束合成、基于逆问题求解的波束合成及基于深度学习的波束合成方法等,以期实现图像质量和成像帧频间更好的权衡。该文综述了平面波成像中的新型波束合成方法领域的研究进展,并对该应用进行了总结和展望。     

基于压缩感知的矢量阵聚焦定位方法

本文针对噪声源近场定位识别问题,利用声源分布在空间域具有稀疏性,在压缩感知理论框架下建立了新体系下的矢量阵聚焦波束形成方法,用于解决同频相干声源的定位识别问题.新方法可在小快拍下准确获得噪声源的空间位置,且不损失对噪声源贡献相对大小的评价能力.通过详细的理论推导、仿真分析和试验验证,证明了基于压缩感知的矢量阵聚焦定位新方法本质上实现了l1范数正则化求解下的波形恢复和空间谱估计,因此具有较高的定位精度,较强的相干声源分辨能力、准确的声源贡献相对大小评价能力以及较高的背景压制能力,可应用于水下复杂噪声源的定位识别.     

Research and application of fast-convergent minimum variance distortionless response algorithm

The conventional MVDR adaptive beamformer is a high-resolution narrowband beamformer which estimates the optimal beamforming weights using narrowband CSDM of real acoustic field.In practical applications,MVDR algorithm needs long observation time to estimate the covariance matrix.This inherent property makes it difficult to localize fast-moving targets.For wideband signals,MVDR algorithm needs inverting every CSDM which increases the computational demands.For correlated sources,the performance of MVDR will degrade dramatically because the signals will cancel each other.A fast-convergent MVDR algorithm based on subband subarray processing is proposed.The full frequency band is divided into sets of subbands and the line array is divided into sets of subarrays.For every subband the STCM of reduced dimensions is calculated.Then adaptive beamforming weight of fast-convergent MVDR algorithm and spatial spectrum estimation are obtained.At the same time,spatial spectrum estimation can be made for correlated sources using the two-sided spatial smoothing method. Results of simulation and trial data show that the proposed method has high-resolution and near-instantaneous convergence property,two-sided spatial smoothing has satisfactory validity of decorrelation.     

用于自适应波束形成语音增强的球谐域掩蔽函数估计方法

提出一种用于球形阵列自适应波束形成的掩蔽函数估计方法。该方法利用包含空间信息的球谐系数提取低维空间向量,并采用复高斯混合模型和深度学习两种方案来估计掩蔽函数,最终利用估计的掩蔽函数设计最小方差无失真响应波束形成器,以达到空域滤波的效果。理论分析和仿真实验证明,对于相同时长的声信号,球谐域掩蔽函数估计方法的计算复杂度比传统阵元域估计方法低了一个数量级。并且在大部分声场环境中,尤其在低信噪比情况下,所提方法的语音质量感知评估测度得分、分段信噪比和短时客观可懂度明显高于阵元域方法,三者最高分别可提升1.31 dB,4.54 dB和35%。另外,实际声学环境的测量实验也验证了所提方法在不影响可懂度的条件下比传统阵元域方法具备更高的降噪量。      

Beamforming for directional sources: additional estimator and evaluation of performance under different acoustic scenarios

Beamforming is done with an array of sensors to achieve a directional or spatially-specific response by using a model of the arriving wavefront. Conventionally, a plane wave or point source model is used and this can cause decreased array gain or even total breakdown of beamforming when the source is directional. To avoid this, the authors proposed in recent work an alternative beamforming method which defines a set of "sub-beamformers," each designed to respond to a different spatial mode of the source. The outputs of the individual sub-beamformers are combined in a weighted sum to give an overall output of better quality than that of a monopole beamformer. This paper extends the previous work by introducing an additional estimator for the weighted sum and by presenting simulation results to demonstrate the relative performance of the proposed method and the different estimators for a directional source in the presence of diffuse noise, reverberation, and an interfering source. Gain optimization subject to a constraint on the white-noise gain with the proposed beamforming method is also introduced. Generally, when beamforming on directional sources, the proposed method outperforms beamforming with a point source model when the input signal-to-noise ratio (SNR) is 0 dB or higher.     

Adaptive beamforming for photoacoustic imaging

An adaptive photoacoustic image reconstruction technique that combines coherence factor (CF) weighting and the minimum variance (MV) method is introduced. The backprojection method is widely used to reconstruct photoacoustic tomography images. Owing to the scattering of light, the quality of the photoacoustic imaging can be degraded. CF, an adaptive weighting technique, is known to improve the lateral resolution of photoacoustic images. In addition, an MV adaptive beamforming method can further improve the image quality by suppressing signals from off-axis directions. Experimental studies are performed to quantify the spatial resolution and contrast of the adaptive photoacoustic beamforming methods.