共查询到17条相似文献,搜索用时 93 毫秒
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针对水下三维成像的空间分辨率难以提高,且具有较高旁瓣级的问题,提出了一种二维解卷积波束形成高分辨三维声成像算法,该算法首先完成任意距离切片的平面阵波束形成,近场情况下采用菲涅尔近似实现近场平面阵波束形成,然后通过二维解卷积技术对任意距离切片的二维波束形成结果进行解卷积处理,去除阵列指向性函数的影响,改善波束响应非理想冲击函数所造成波束形成主瓣宽及旁瓣级高的问题。通过计算机仿真分析,新算法可以有效的提高水下三维成像的空间分辨率,抑制旁瓣级,并能够在较宽频带和不同阵列孔径内保持与常规波束形成相当的稳定性。通过试验研究,新方法比常规波束形成实际目标成像分辨率提高一倍,最高旁瓣级下降20 dB,验证了该算法在实际系统中的有效性. 相似文献
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提出了定位远近场混合源的波束解卷积技术,针对非相干远近场混合声信号的线列阵观测结果,推导了其常规波束形成(CBF)空间谱中固有的广义二维卷积数学关系,利用Richardson-Lucy算法实现波束能量聚焦以获得近场目标的精确空域参数估计,通过混合源协方差矩阵向近场流形的正交补空间投影操作提取远场分量,并分析得到其内在的一维卷积关系,然后通过角度域波束解卷积进行远场信号的波达估计。仿真分析表明,所提方法提升了CBF谱的空域分辨力,通过投影映射隔离近场分量后实现了混合源的分离。与现有方案相比,所提算法针对远场信源可实现10 dB的背景噪声级抑制。 相似文献
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反卷积是实现光谱图超分辨复原的重要手段,与常规反卷积相比,盲目反卷积具有不需要预先准确获取卷积核函数的优势。着眼于充分利用光谱信号的特点和已有的光谱图反卷积成果,详细讨论了空域迭代盲目反卷积方法用于光谱图反卷积时的算法实现问题,并在分析光谱图卷积退化过程的基础上,针对光谱图反卷积算法特点,提出了光谱图卷积退化简化计算模型和最小二乘高斯拟合模型,以解决算法中相应的计算问题。基于Matlab平台的仿真表明,对于所用的高斯型谱线和点扩散函数,空域迭代盲目反卷积算法效果良好,在信噪比为50 dB时,分辨率提高约30%。 相似文献
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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。 相似文献
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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。 相似文献
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研究了从声散射场的远场分布的信息来再现声阻抗障碍物形状的反问题,建立了求解这类反问题的一种非线性最优化模型,并提出了数值实现该非线性最优化模型的一种两步调整迭代算法.两步过程的应用使在确定未知障碍物形状的非线性最优化步中未知函数的个数达到了最少,而在调整迭代过程中,通过利用前一迭代步所得重构信息,使重构精度得到了相当大的改进.所建立的反演算法的一个特别吸引人的性质是,只需要远场分布的一个Fourier系数即可对未知声阻抗障碍物作几何物形的设别.对大量具有各种几何形状的二维障碍物的数值算例保证了本算法是实用和有效的. 相似文献
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由条码扫描仪获得条码图像的过程可以用理想条码信号与扫描仪光学系统点扩散函数的卷积模型来描述。反卷积是消除由光学系统点扩散带来的模糊现象的最好办法。为克服反卷积的病态问题,研究了反卷积的正则化方法;针对条码信号的特点,构建了适合于条码信号复原的惩罚项,提出了条码信号的正则化复原算法及其适合于计算机运算的迭代算法。通过实验研究了算法在不同情况下的抗干扰能力。实验结果表明,正则化条码信号复原算法在消除系统点扩散函数的影响的同时能够很好地抑制噪声。 相似文献
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Paillasseur S Thomas JH Pascal JC 《The Journal of the Acoustical Society of America》2011,129(6):3777-3787
Near-field acoustic holography is a measuring process for locating and characterizing stationary sound sources from measurements made by a microphone array in the near-field of the acoustic source plane. A technique called real-time near-field acoustic holography (RT-NAH) has been introduced to extend this method in the case of nonstationary sources. This technique is based on a formulation which describes the propagation of time-dependent sound pressure signals on a forward plane using a convolution product with an impulse response in the time-wavenumber domain. Thus the backward propagation of the pressure field is obtained by deconvolution. Taking the evanescent waves into account in RT-NAH improves the spatial resolution of the solution but makes the deconvolution problem "ill-posed" and often yields inappropriate solutions. The purpose of this paper is to focus on solving this deconvolution problem. Two deconvolution methods are compared: one uses a singular value decomposition and a standard Tikhonov regularization and the other one is based on optimum Wiener filtering. A simulation involving monopoles driven by nonstationary signals demonstrates, by means of objective indicators, the accuracy of the time-dependent reconstructed sound field. The results highlight the advantage of using regularization and particularly in the presence of measurement noise. 相似文献
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有源声呐感兴趣的参量是目标距离和径向速度,它们无法直接观测得到,需要通过估计而获得。利用波导多路径环境多目标时延-多普勒模型,可以导出采样互模糊度函数均值是发射信号自模糊度函数与广义目标反射性密度函数的两维卷积,其中广义目标反射性密度函数为信道扩展函数与目标反射性密度函数的两维卷积。依据信息理论最小Csiszar鉴别准则,可导出R-L (Richardson-Lucy)迭代解卷算法,对采样互模糊度函数均值进行两维迭代解卷积,消除发射信号和信道引入的模糊,序贯地实现时延-多普勒两维像的估计,进而获得多目标的时延和多普勒参量估计。仿真结果和海上实验数据分析验证了R-L解卷算法的可行性和有效性,较之常规的匹配滤波和维纳滤波算法,R-L算法有效地提高了时延和多普勒估计的分辨力和精度。 相似文献
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《声学学报:英文版》2017,(4)
The ranges and radial velocities of targets are interesting parameters in the active sonar, which can't be observed directly but can only be estimated. Firstly, by making use of the delay-Doppler model of multi-targets in waveguide multipath environment, one finds that sample cross-ambiguity function is a two-dimensional(2D) convolution of the auto-ambiguity function of the transmitted signal with the generalized target reflectivity density, which is a 2D convolution of the spread function of channel with the reflectivity density as well. Secondly,from the perspective of information theory, an iterative deconvolution algorithm named R-L(Richardson-Lucy) is derived based on minimum Csiszar discrimination criterion. Finally, the blurs caused by both of the transmitted signal and channel are removed by 2D deconvolution of the expectation of sample cross-ambiguity function, 2D image and then parameters of time-delay and Doppler is estimated sequentially. Results of both numerical simulation and sea experimental data processing verify the feasibility and effectiveness of R-L deconvolution algorithm, which effectively improves the resolution and precision of the time delay and Doppler estimation, when compared to the classical match filtering and Wiener filtering. 相似文献
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多帧盲解卷积图像复原技术能够进一步提高自适应光学图像的分辨力,但其算法比较复杂,处理耗时过长,对序列图像复原经常需要几分钟甚至几十分钟的计算时间,对实际应用造成了极大不便。为了提升算法的运行速度,改善其耗时过长的问题,通过研究和分析盲解卷积算法原理和算法结构,采用目前高速发展的中央处理器(CPU)和图形处理器(GPU)异构加速技术,主要对耗时最长的矩阵卷积运算进行优化,通过使用库函数与算法结构微调相结合的方法并行加速,实现多帧盲解卷积的图像复原算法的并行化。使用并行算法对图像进行复原处理,针对16帧以上分辨率为256256像素的空间目标图像,可以实现17的加速比,为图像复原的实时/准实时提供一种可行的方案。 相似文献
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Although the use of blind deconvolution of image restoration is a widely known concept, only few reports have discussed in
detail its application to solving problem of restoration of underwater range-gated laser images. A comparative study of underwater
image restoration using the Richardson-Lucy algorithm, the least-squares algorithm, and the multiplicative iterative algorithm
for blind deconvolution is presented. All the deconvolution approaches use denoised underwater images and Wells’ small angle
approximation theory of derived point spread function as the initial object and degradation guess, respectively. Owing the
underwater no-reference imaging environment, image quality judgment based on the blur metric method is incorporated in our
comparison to determine the appropriate deconvolution iteration number for each algorithm, which objectively evaluates the
image restoration results. The performance of the three algorithms applied to underwater image restoration is discussed and
reported. 相似文献