球形传声器阵列下基于主导声源检测MUSIC群时延算法的多声源定位*

针对混响环境中,多径效应、散射、衍射等原因导致声源定位失败或分辨能力不足的现象,提出一种基于主导声源检测MUSIC群时延的邻近多声源定位方法。该方法采用球形传声器阵列,相比平面阵列可以捕获3D声场信息,利用球谐域下信号的频率分量与角度分量解耦的优势,从而可直接利用频率平滑技术处理宽带语声信号而不需要构造聚焦矩阵,并在球谐域下通过设置阈值对一组时频段进行主导声源检测,从而选择出包含直达声的一组时频块来构造MUSIC群时延空间谱。上述举措在提升波达方向估计在高混响环境下定位鲁棒性的同时,也提高了多个邻近声源的分辨能力。仿真实验结果表明,所提出的主导声源检测MUSIC群时延算法,在高混响和低信噪比条件下,仍具有更好的定位精度与更优的邻近多声源分辨效果。     

采用球谐分解的二范数广义逆波束形成

基于传声器阵列的声成像技术是解决噪声源识别的有效途径之一.该文提出了一种基于球谐分解的L2范数广义逆波束形成算法,并对此算法在分布式球形阵列布放方案下进行了定位精度及鲁棒性的对比分析研究.仿真结果显示,此算法对低频相干声源具有较高的空间定位精确度,且阵元位置误差对此算法性能的影响有限.通过在半消声室进行实验进一步证明了...     

一种分布式双麦克风线阵声源定位方法

分布式麦克风阵列由于比传统的单阵列具有更大的空间孔径,可以获得更好的声源定位性能,因此基于分布式麦克风阵列的声源定位方法成为当前麦克风阵列领域研究的热点之一。本文研究了一种基于分布式双麦克风线阵的声源定位方法,并进行了系统实现,从理论上剖析了该算法的定位精度与单阵列定向误差以及声源位置之间的关系,而且还揭示了声源高度扰动对该算法定位精度的影响。最后,分别通过仿真实验和实际定位系统的测试结果,验证了本文理论分析的正确性。     

阵元随机均匀分布球面阵列联合噪声源定位方法

基于球面传声器阵列的噪声源定位方法,设计加工了阵元随机均匀分布64元球面传声器阵列,研究了球面近场声全息和球谐函数模态展开聚焦波束形成联合噪声源定位识别方法,对算法的性能进行了仿真分析,并利用球面传声器阵列进行了噪声源的定位识别试验.研究表明,阵元随机均匀分布球面阵列具有全空间稳定的目标定位性能,球面近场声全息对低频近距离声源具有较高的定位精度,球谐函数模态展开聚焦波束形成对高频远距离声源具有较高的定位精度,将两种方法联合进行声源的定位识别,可以在较小孔径的球面阵列和较少阵元的条件下,在宽频带范围内获得对目标声源良好的定位性能.     

球谐域自适应混响抵消与声源定位算法

提出了一种基于球谐域的自适应混响抵消与声源定位算法,该方法通过去混响处理改善语音质量,并提高球谐域定位算法在混响环境下的定位性能。推导了基于多通道线性预测的自适应混响抵消算法在球谐域的表达式,针对刚球模型提出分阶处理的去混响方法,并对去混响后的信号进行波达方向估计。采用32元球阵的仿真结果表明,相比于球谐域不分阶去混响方法,该方法最大可减少约2/3的运算量,同时语音PESQ得分及SRMR均显著提高。利用实验数据对算法性能进行测试,实验结果验证了该方法在实际声学环境中去混响和声源定位的有效性。      

无网格方法中结点搜索算法的改进

在采用无网格方法进行数值计算时,常常涉及到结点搜索问题.在处理复杂区域问题时,求解区域中分布的结点数量非常大.如果用传统的全局结点搜索算法时计算量将十分巨大,因此,提出了求解域分解法以减小结点搜索的计算量.采用该方法时,结点搜索的范围就可以由整个求解域缩减到几个相关的子域中,从而大大地减少了无网格方法计算中的结点搜索时间.用该方法对理想流体的位势流动进行了数值模拟,发现:随结点数的增加,无网格方法所用时间与有限元方法相比越来越大;但与全局结点搜索无网格方法相比,本方法大大节省了计算时间.     

导向相应功率定位中时延不确定项的影响

在分布式传声器网络应用中,可控波束形成器导向相应功率(SRP)声源定位算法受到导向时延不准确的影响导致定位不稳定.将导向时延不确定项引入SRP定位模型,并对引起不确定项的主要干扰因素进行了分析建模,同时提出了应对导向时延不确定项的SRP定位算法.蒙特卡罗仿真和分析表明该算法具有抑制噪声和降低定位误差的效果,实际实验数据...     

基于旁瓣级最小化的平面螺旋阵列设计的粒子群优化方法

基于粒子群优化算法,以最大旁瓣级最小化为代价函数,在阿基米德螺旋线上搜索最优平面螺旋阵列。数值仿真计算利用粒子群优化算法搜索得到旁瓣级最低的最优阵列,与其他阵型比较,并在半消声室对各种声源进行声场测量和声源定位实验,验证了粒子群优化方法进行阵型优化的有效性。     

基于倾斜阵的距离与方位联合估计*

针对阵列倾斜引起的阵不变量方法定位精度下降的问题,该文结合倾斜阵声源定位的三维模型提出一种基于倾斜阵的距离与方位联合估计算法。该算法通过联合自校正方位距离联合估计方法对阵列倾角在声源-接收平面的投影量进行估计和补偿,改善目标距离估计误差的同时,利用匹配出的阵列倾角投影量反向估计目标方位,获得对声源二维位置的估计。通过仿真验证了该算法能够在阵列倾角较小时实现目标方位与距离的联合估计。     

基于蚁群算法的室内可见光高精度三维定位系统

为了提高室内定位精度,实现三维定位,提出一种基于蚁群算法的的可见光通信室内高精度三维定位系统。本系统采用了码分多址(Code Division Multiple Access,CDMA)调制技术,解决了室内可见光通信多信号源之间的符号间干扰.系统中与发光二极管(Light Emitting Diode,LED)光信号源地理位置相关的ID信息码经过直接扩频调制后加载至发光二极管驱动电路,以光信号的形式在室内传播.光信号经过放大、滤波、采样处理后,根据码分多址调制技术中扩频码的正交性恢复出ID信息及光强衰减信息.经过计算获得来自不同发光二极管的信号光强衰减因子,利用蚁群算法的全局搜索性确定最优定位点.引入误差修复因子,利用蚁群算法的并行搜索性对光强衰减因子偏差进行修正.仿真结果表明,信噪比为30dB,20dB,10dB的条件下,算法的定位精度分别为2cm,4cm,8cm.当计算的精度高于45cm时,蚁群算法定位解的搜索效率明显高于遍历法.在10dB的信噪比条件下,对光强衰减因子进行修正后100%的测试点都实现了5cm定位精度.实验结果表明,20dB信噪比条件下,92.59%的测试点的定位误差小于8cm,96.29%的测试点定位误差小于10cm,最大定位误差为11.30cm.经过误差修复后,96.2%的测试点实现了3cm的定位精度,61.6%的测试点实现了2cm的定位精度.本算法在实现了高精度定位,减少了获得最优定位解的计算量.     

Spherical harmonics MUSIC versus conventional MUSIC

With the three-dimensional symmetry and wide potential application, spherical array signal processing has been a hot research area for years. This paper devotes to the direction-of-arrival (DOA) estimation of the spherical arrays. Based on the orthogonality of the sensors’ location, MUSIC algorithm in spherical space is proposed, named as SH-MUSIC. Similar to beamspace MUSIC, spherical harmonics transformation is operated before MUSIC algorithm and a better performance is gotten because SH-MUSIC utilizes the array configuration’s orthogonality. On account of the transformation matrix’s orthogonality, spherical harmonics transformation is suggested to be operated firstly in other improved MUSIC algorithms without rejection, and it is demonstrated in beamspace MUSIC. In addition, owing to the tiny error between the steering vectors and the spherical harmonics with high order, sphere array data models including open sphere and rigid sphere are constructed. Simulation proves SH-MUSIC to be effective. Moreover, experimental data from a rigid sphere microphone array is dealt with by SH-MUSIC and the DOAs are estimated accurately.     

采用听觉滤波器的宽带MUSIC声源定位方法

在分析了采用短时傅里叶变换的宽带MUSIC声源定位算法(SF-MUSIC)存在问题的基础上,提出了一种采用听觉滤波器的宽带MUSIC声源定位算法(AF-MUSIC)。该算法使用听觉滤波器组对传声器阵列接收到的信号进行不等带宽分解后,在各个频率通道上使用MUSIC算法进行声源定位,并结合子区间频数估计法得出最终定位结果。对算法进行的实验评估表明,在不同声源类型条件下,相比SF-MUSIC算法,AF-MUSIC算法的平均估计误差减少2.5479°,有效地提高了声源波达方向估计的精度。      

Registering spherical navigators with spherical harmonic expansions to measure three-dimensional rotations in magnetic resonance imaging

Subject motion remains a challenging problem to overcome in clinical and research applications of magnetic resonance imaging (MRI). Subject motion degrades the quality of MR images and the integrity of experimental data. A promising method to correct for subject motion in MRI is the spherical navigator (SNAV) echo. Spherical navigators acquire k-space data on the surface of a sphere in order to measure three-dimensional (3D) rigid-body motion. Analysis begins by registering the magnitude of two SNAVs to determine the 3D rotation between them. Several different methods to register SNAV data exist, each with specific capabilities and limitations. In this study, we assessed the accuracy, precision and computational requirements of measuring rotations about all three coordinate axes by correlating the spherical harmonic expansions of SNAV data. We compare the results of this technique to previous SNAV studies and show that, although computationally expensive, the spherical harmonic technique is a highly accurate, precise and robust method to register SNAVs and detect 3D rotations in MRI. A key advantage to the spherical harmonic technique is the ability to optimize the accuracy, precision, processing time and memory requirements by adjusting parameters used in the registration. While present developments are aimed at improving the programming efficiency and memory handling of the algorithm, this registration technique is currently well suited for retrospective motion correction applications, such as removing motion-related image artifacts and aligning slices within a high-resolution 3D volume.     

基于Group Lasso的多重信号分类声源 定位优化算法*

多重信号分类算法因其抑制噪声能力强、计算速度快等优点,在声源定位领域得到广泛应用。但该算法在中低频段分辨率及聚焦性能较差。针对该问题,提出一种基于Group Lasso的多重信号分类优化算法。该算法将多重信号分类算法输出值作为初始值,并在Group Lasso算法组间计算时对目标信号进行稀疏、在组内计算时对该组信号进行平滑及阈值截断。仿真结果表明:该优化算法在中低频段可明显提高多重信号分类算法分辨率,同时改善因扫描位置与声源面位置不重合引起的聚焦性能下降问题。     

Fast algorithms for spherical harmonic expansions,III

We accelerate the computation of spherical harmonic transforms, using what is known as the butterfly scheme. This provides a convenient alternative to the approach taken in the second paper from this series on “Fast algorithms for spherical harmonic expansions”. The requisite precomputations become manageable when organized as a “depth-first traversal” of the program’s control-flow graph, rather than as the perhaps more natural “breadth-first traversal” that processes one-by-one each level of the multilevel procedure. We illustrate the results via several numerical examples.     

运动声源快速定位的声达时差法

针对声达时差法只能用于非运动声源定位的问题,本文提出一种运动声源快速定位方法。该方法以声达时差为基本定位原理,基于声源计算位置对多普勒效应进行解耦并进行声信号多普勒效应修正,根据三角定位方法构建声传播空间矩阵,以声源位置偏差度为目标基于单纯形优化搜索算法进行声源位置快速逼近,实现了对匀速直线运动的单声源的定位追踪,提高定位实时性。该方法将声达时差法拓展到运动声源的定位,同时解决了消除多普勒效应带来的计算过程复杂、运算量大的问题,仅用4个传声器就可实现运动声源的快速定位,突破了传统运动声源识别中对大传声器阵列的依赖。仿真实验和实车运动声源识别实验结果证明了该方法的有效性,本研究为短时发声运动声源的识别提供了一种简便、高效的方法。      

DOA estimation for uniform circular array without the source number based on beamspace transform and higher-order cumulant

Fourth-order cumulant is one of most widely used high-order cumulant for direction of arrival (DOA) estimation due to its ability of expanding the virtual array aperture as well as suppressing Gaussian noise. To address the two-dimensional (2D) DOA estimation problem, we propose a modified MUSIC scheme for uniform circular array (UCA) in this paper. Firstly, the fourth-order cumulant of UCA is considered to construct a new propagator, resulting in the elimination of a priori knowledge of the number of signals. Secondly, the UCA is transformed by beamspace transformation, reducing the time computational complexity of the algorithm since the two-dimensional grid search and singular value decomposition are avoided. And finally a low-rank recovery algorithm is adopted to improve the accuracy regarding the limited snapshots scenario. The numerical simulations validate the superiority of the proposed method.