This paper is mainly to deal with the problem of direction of arrival(DOA) estimations of multiple narrow-band sources impinging on a uniform linear array under impulsive noise environments. By modeling the impulsive ...This paper is mainly to deal with the problem of direction of arrival(DOA) estimations of multiple narrow-band sources impinging on a uniform linear array under impulsive noise environments. By modeling the impulsive noise as α-stable distribution, new methods which combine the sparse signal representation technique and fractional lower order statistics theory are proposed. In the new algorithms, the fractional lower order statistics vectors of the array output signal are sparsely represented on an overcomplete basis and the DOAs can be effectively estimated by searching the sparsest coefficients. To enhance the robustness performance of the proposed algorithms,the improved algorithms are advanced by eliminating the fractional lower order statistics of the noise from the fractional lower order statistics vector of the array output through a linear transformation. Simulation results have shown the effectiveness of the proposed methods for a wide range of highly impulsive environments.展开更多
Performance of Turbo-Codes in communication channels with impulsive noise is analyzed. First, mathematical model of impulsive noise is presented because it has non-Gaussian nature and is found in many wireless channel...Performance of Turbo-Codes in communication channels with impulsive noise is analyzed. First, mathematical model of impulsive noise is presented because it has non-Gaussian nature and is found in many wireless channels due to impulsive phenomena of radio-frequency interference. Then, with linear Log-MAP decoding algorithm for its low complexity, Turbo-Codes are adopted and analyzed in such communication channels. To confirm the performance of the proposed method, simulations on both static and fully interleaved flat Rayleigh fading channels with impulsive noise have been carried out. It is shown that Turbo-Codes have a better performance than the conventional methods （e.g. convolutionally coded system）.展开更多
This work aims to explore the restoration of images corrupted by impulse noise via distribution-transformed network (DTN), which utilizes convolutional neural network to learn pixel-distribution features from noisy im...This work aims to explore the restoration of images corrupted by impulse noise via distribution-transformed network (DTN), which utilizes convolutional neural network to learn pixel-distribution features from noisy images. Compared with the traditional median-based algorithms, it avoids the complicated pre-processing procedure and directly tackles the original image. Additionally, different from the traditional methods utilizing the spatial neighbor information around the pixels or patches and optimizing in an iterative manner, this work turns to capture the pixel-level distribution information by means of wide and transformed network learning. DTN fits the distribution at pixel-level with larger receptions and more channels. Furthermore, DTN utilities a residual block without batch normalization layer to generate a good estimate. In terms of edge preservation and noise suppression, the proposed DTN consistently achieves significantly superior performance than current state-of-the-art methods, particularly at extreme noise densities.展开更多
为了解决冲击噪声下长短时记忆(long short term memory,LSTM)神经网络调制信号识别方法抗冲击噪声能力弱和超参数难以确定的问题,本文提出了一种演化长短时记忆神经网络的调制识别方法。利用基于短时傅里叶变换的卷积神经网络(convolut...为了解决冲击噪声下长短时记忆(long short term memory,LSTM)神经网络调制信号识别方法抗冲击噪声能力弱和超参数难以确定的问题,本文提出了一种演化长短时记忆神经网络的调制识别方法。利用基于短时傅里叶变换的卷积神经网络(convolution neural network,CNN)去噪模型对数据集去噪;结合量子计算机制和旗鱼优化器(sailfish optimizer,SFO)设计了量子旗鱼算法(quantum sailfish algorithm,QSFA)去演化LSTM神经网络以获得最优的超参数;使用演化长短时记忆神经网络作为分类器进行自动调制信号识别。仿真结果表明,采用所设计的CNN去噪和演化长短时记忆神经网络模型,识别准确率有了大幅度的提高。量子旗鱼算法演化LSTM神经网络模型降低了传统LSTM神经网络容易陷于局部极小值或者过拟合的概率,当混合信噪比为0 dB,所提方法对11种调制信号的平均识别准确率达到90%以上。展开更多
针对现有相干分布源波达方向(Direction Of Arrival,DOA)估计方法计算量大、抗冲击噪声能力弱和不能有效去相干等难题,本文提出了一种冲击噪声下相干分布源多峰DOA估计方法,并推导了冲击噪声下相干分布源DOA估计的克拉美罗界.为了实现...针对现有相干分布源波达方向(Direction Of Arrival,DOA)估计方法计算量大、抗冲击噪声能力弱和不能有效去相干等难题,本文提出了一种冲击噪声下相干分布源多峰DOA估计方法,并推导了冲击噪声下相干分布源DOA估计的克拉美罗界.为了实现冲击噪声下相干分布源DOA估计,采用加权范数协方差抑制冲击噪声,进而首次推导出多峰加权信号子空间拟合方程,并设计了一种多峰量子秃鹰算法快速无量化误差求解.仿真结果表明,所提方法在冲击噪声下能够以较小的快拍数实现相干分布源DOA估计,且无需额外的解相干操作即可有效去相干.与一些已有的高精度DOA估计方法相比,所提方法仿真时间明显缩短,且具有更高的估计精度和估计成功概率,突破了已有相干分布源DOA估计方法的应用局限,可推广应用于其他复杂的DOA估计问题中.展开更多
脉冲噪声广泛存在于电力线通信(power line communication,PLC)系统中,会严重影响系统的通信性能。电力线脉冲噪声的建模通常使用α稳定分布模型,为达到最佳的脉冲噪声抑制效果,需要知道脉冲噪声的类型和相关参数。为此,文章提出一种基...脉冲噪声广泛存在于电力线通信(power line communication,PLC)系统中,会严重影响系统的通信性能。电力线脉冲噪声的建模通常使用α稳定分布模型,为达到最佳的脉冲噪声抑制效果,需要知道脉冲噪声的类型和相关参数。为此,文章提出一种基于混合神经网络的符合α稳定分布的脉冲噪声参数估计方法。不同于传统的方法,本方法可以分别独立地估计α稳定分布的重要参数α(即特征指数)和γ(即尺度参数)。仿真结果表明,与传统方法相比,提出的方法具有更准确的参数估计性能,归一化均方误差值仅为10–4左右。展开更多
该文面向存在脉冲干扰的配电网信号环境,研究脉冲干扰检测、精确定位和基于干扰剔除的同步相量测量算法。首先,根据同步相量测量单元(phasormeasurementunit,PMU)测量值进行基波重构,通过分析重构残差序列定义特征参数残差总向量(total ...该文面向存在脉冲干扰的配电网信号环境,研究脉冲干扰检测、精确定位和基于干扰剔除的同步相量测量算法。首先,根据同步相量测量单元(phasormeasurementunit,PMU)测量值进行基波重构,通过分析重构残差序列定义特征参数残差总向量(total vector of the residual,TVR),用以表征PMU测量误差。其次,提出基于TVR和残差瞬时功率异常值判别的方法,实现脉冲干扰检测和精确定位。最后,基于相量测量的最优滤波原理设计出改进加权最小二乘算法,实现脉冲干扰下同步相量的快速准确测量。仿真结果表明,在数据窗长为2个周波(P类测量),当脉冲干扰长度不超过半个周波时,该文算法在典型配电网环境下达到IEEE标准规定的测量误差要求(TVE<1%),且对脉冲干扰持续时间和强度表现出较强的鲁棒性。展开更多
基金supported in part by the National Natural Science Foundation of China(61301228,61371091)the Fundamental Research Funds for the Central Universities(3132014212)
文摘This paper is mainly to deal with the problem of direction of arrival(DOA) estimations of multiple narrow-band sources impinging on a uniform linear array under impulsive noise environments. By modeling the impulsive noise as α-stable distribution, new methods which combine the sparse signal representation technique and fractional lower order statistics theory are proposed. In the new algorithms, the fractional lower order statistics vectors of the array output signal are sparsely represented on an overcomplete basis and the DOAs can be effectively estimated by searching the sparsest coefficients. To enhance the robustness performance of the proposed algorithms,the improved algorithms are advanced by eliminating the fractional lower order statistics of the noise from the fractional lower order statistics vector of the array output through a linear transformation. Simulation results have shown the effectiveness of the proposed methods for a wide range of highly impulsive environments.
文摘Performance of Turbo-Codes in communication channels with impulsive noise is analyzed. First, mathematical model of impulsive noise is presented because it has non-Gaussian nature and is found in many wireless channels due to impulsive phenomena of radio-frequency interference. Then, with linear Log-MAP decoding algorithm for its low complexity, Turbo-Codes are adopted and analyzed in such communication channels. To confirm the performance of the proposed method, simulations on both static and fully interleaved flat Rayleigh fading channels with impulsive noise have been carried out. It is shown that Turbo-Codes have a better performance than the conventional methods （e.g. convolutionally coded system）.
基金the National Natural Science Founding of China (Nos. 61362001, 61362009 and 61661031)the Jiangxi Advanced Project for Post-Doctoral Research Fund (No. 2014KY02)+1 种基金the Young and Key Scientist Training Plan of Jiangxi Province (Nos. 20162BCB23019, 20171BBH80023 and GJJ170566)the Fund for Postgraduate of Nanchang University (No. CX2018144)。
文摘This work aims to explore the restoration of images corrupted by impulse noise via distribution-transformed network (DTN), which utilizes convolutional neural network to learn pixel-distribution features from noisy images. Compared with the traditional median-based algorithms, it avoids the complicated pre-processing procedure and directly tackles the original image. Additionally, different from the traditional methods utilizing the spatial neighbor information around the pixels or patches and optimizing in an iterative manner, this work turns to capture the pixel-level distribution information by means of wide and transformed network learning. DTN fits the distribution at pixel-level with larger receptions and more channels. Furthermore, DTN utilities a residual block without batch normalization layer to generate a good estimate. In terms of edge preservation and noise suppression, the proposed DTN consistently achieves significantly superior performance than current state-of-the-art methods, particularly at extreme noise densities.
文摘为了解决冲击噪声下长短时记忆(long short term memory,LSTM)神经网络调制信号识别方法抗冲击噪声能力弱和超参数难以确定的问题,本文提出了一种演化长短时记忆神经网络的调制识别方法。利用基于短时傅里叶变换的卷积神经网络(convolution neural network,CNN)去噪模型对数据集去噪;结合量子计算机制和旗鱼优化器(sailfish optimizer,SFO)设计了量子旗鱼算法(quantum sailfish algorithm,QSFA)去演化LSTM神经网络以获得最优的超参数;使用演化长短时记忆神经网络作为分类器进行自动调制信号识别。仿真结果表明,采用所设计的CNN去噪和演化长短时记忆神经网络模型,识别准确率有了大幅度的提高。量子旗鱼算法演化LSTM神经网络模型降低了传统LSTM神经网络容易陷于局部极小值或者过拟合的概率,当混合信噪比为0 dB,所提方法对11种调制信号的平均识别准确率达到90%以上。
文摘针对现有相干分布源波达方向(Direction Of Arrival,DOA)估计方法计算量大、抗冲击噪声能力弱和不能有效去相干等难题,本文提出了一种冲击噪声下相干分布源多峰DOA估计方法,并推导了冲击噪声下相干分布源DOA估计的克拉美罗界.为了实现冲击噪声下相干分布源DOA估计,采用加权范数协方差抑制冲击噪声,进而首次推导出多峰加权信号子空间拟合方程,并设计了一种多峰量子秃鹰算法快速无量化误差求解.仿真结果表明,所提方法在冲击噪声下能够以较小的快拍数实现相干分布源DOA估计,且无需额外的解相干操作即可有效去相干.与一些已有的高精度DOA估计方法相比,所提方法仿真时间明显缩短,且具有更高的估计精度和估计成功概率,突破了已有相干分布源DOA估计方法的应用局限,可推广应用于其他复杂的DOA估计问题中.
文摘脉冲噪声广泛存在于电力线通信(power line communication,PLC)系统中,会严重影响系统的通信性能。电力线脉冲噪声的建模通常使用α稳定分布模型,为达到最佳的脉冲噪声抑制效果,需要知道脉冲噪声的类型和相关参数。为此,文章提出一种基于混合神经网络的符合α稳定分布的脉冲噪声参数估计方法。不同于传统的方法,本方法可以分别独立地估计α稳定分布的重要参数α(即特征指数)和γ(即尺度参数)。仿真结果表明,与传统方法相比,提出的方法具有更准确的参数估计性能,归一化均方误差值仅为10–4左右。
文摘该文面向存在脉冲干扰的配电网信号环境,研究脉冲干扰检测、精确定位和基于干扰剔除的同步相量测量算法。首先,根据同步相量测量单元(phasormeasurementunit,PMU)测量值进行基波重构,通过分析重构残差序列定义特征参数残差总向量(total vector of the residual,TVR),用以表征PMU测量误差。其次,提出基于TVR和残差瞬时功率异常值判别的方法,实现脉冲干扰检测和精确定位。最后,基于相量测量的最优滤波原理设计出改进加权最小二乘算法,实现脉冲干扰下同步相量的快速准确测量。仿真结果表明,在数据窗长为2个周波(P类测量),当脉冲干扰长度不超过半个周波时,该文算法在典型配电网环境下达到IEEE标准规定的测量误差要求(TVE<1%),且对脉冲干扰持续时间和强度表现出较强的鲁棒性。
