Underwater acoustic signal processing is one of the research hotspots in underwater acoustics.Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing.Owing to the complexity ...Underwater acoustic signal processing is one of the research hotspots in underwater acoustics.Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing.Owing to the complexity of marine environment and the particularity of underwater acoustic channel,noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing.In order to solve the dilemma,we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),minimum mean square variance criterion(MMSVC) and least mean square adaptive filter(LMSAF).This noise reduction technique,named CEEMDAN-MMSVC-LMSAF,has three main advantages:(i) as an improved algorithm of empirical mode decomposition(EMD) and ensemble EMD(EEMD),CEEMDAN can better suppress mode mixing,and can avoid selecting the number of decomposition in variational mode decomposition(VMD);(ii) MMSVC can identify noisy intrinsic mode function(IMF),and can avoid selecting thresholds of different permutation entropies;(iii) for noise reduction of noisy IMFs,LMSAF overcomes the selection of deco mposition number and basis function for wavelet noise reduction.Firstly,CEEMDAN decomposes the original signal into IMFs,which can be divided into noisy IMFs and real IMFs.Then,MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs.Finally,both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained.Compared with other noise reduction techniques,the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals,which has the better noise reduction effect and has practical application value.CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection,feature extraction,classification and recognition of underwater acoustic signals.展开更多
Nano-volt magnetic resonance sounding(MRS) signals are sufficiently weak so that during the actual measurement, they are affected by environmental electromagnetic noise, leading to inaccuracy of the extracted characte...Nano-volt magnetic resonance sounding(MRS) signals are sufficiently weak so that during the actual measurement, they are affected by environmental electromagnetic noise, leading to inaccuracy of the extracted characteristic parameters and hindering effective inverse interpretation. Considering the complexity and non-homogeneous spatial distribution of environmental noise and based on the theory of adaptive noise cancellation, a model system for noise cancellation using multi-reference coils was constructed to receive MRS signals. The feasibility of this system with theoretical calculation and experiments was analyzed and a modified sigmoid variable step size least mean square(SVSLMS) algorithm for noise cancellation was presented. The simulation results show that, the multi-reference coil method performs better than the single one on both signal-to-noise ratio(SNR) improvement and signal waveform optimization after filtering, under the condition of different noise correlations in the reference coils and primary detecting coils and different SNRs. In particular, when the noise correlation is poor and the SNR<0, the SNR can be improved by more than 8 dB after filtering with multi-reference coils. And the average fitting errors for initial amplitude and relaxation time are within 5%. Compared with the normalized least mean square(NLMS) algorithm and multichannel Wiener filter and processing field test data, the effectiveness of the proposed method is verified.展开更多
离群点检测任务是指检测与正常数据在特征属性上存在显著差异的异常数据。大多数基于聚类的离群点检测方法主要从全局角度对数据集中的离群点进行检测,而对局部离群点的检测性能较弱。基于此,本文通过引入快速搜索和发现密度峰值方法改...离群点检测任务是指检测与正常数据在特征属性上存在显著差异的异常数据。大多数基于聚类的离群点检测方法主要从全局角度对数据集中的离群点进行检测,而对局部离群点的检测性能较弱。基于此,本文通过引入快速搜索和发现密度峰值方法改进K-means聚类算法,提出了一种名为KLOD(local outlier detection based on improved K-means and least-squares methods)的局部离群点检测方法,以实现对局部离群点的精确检测。首先,利用快速搜索和发现密度峰值方法计算数据点的局部密度和相对距离,并将二者相乘得到γ值。其次,将γ值降序排序,利用肘部法则选择γ值最大的k个数据点作为K-means聚类算法的初始聚类中心。然后,通过K-means聚类算法将数据集聚类成k个簇,计算数据点在每个维度上的目标函数值并进行升序排列。接着,确定数据点的每个维度的离散程度并选择适当的拟合函数和拟合点,通过最小二乘法对升序排列的每个簇的每1维目标函数值进行函数拟合并求导,以获取变化率。最后,结合信息熵,将每个数据点的每个维度目标函数值乘以相应的变化率进行加权,得到最终的异常得分,并将异常值得分较高的top-n个数据点视为离群点。通过人工数据集和UCI数据集,对KLOD、LOF和KNN方法在准确度上进行仿真实验对比。结果表明KLOD方法相较于KNN和LOF方法具有更高的准确度。本文提出的KLOD方法能够有效改善K-means聚类算法的聚类效果,并且在局部离群点检测方面具有较好的精度和性能。展开更多
In this paper a square wavelet thresholding method is proposed and evaluated as compared to the other classical wavelet thresholding methods (like soft and hard). The main advantage of this work is to design and imple...In this paper a square wavelet thresholding method is proposed and evaluated as compared to the other classical wavelet thresholding methods (like soft and hard). The main advantage of this work is to design and implement a new wavelet thresholding method and evaluate it against other classical wavelet thresholding methods and hence search for the optimal wavelet mother function among the wide families with a suitable level of decomposition and followed by a novel thresholding method among the existing methods. This optimized method will be used to shrink the wavelet coefficients and yield an adequate compressed pressure signal prior to transmit it. While a comparison evaluation analysis is established, A new proposed procedure is used to compress a synthetic signal and obtain the optimal results through minimization the signal memory size and its transmission bandwidth. There are different performance indices to establish the comparison and evaluation process for signal compression;but the most well-known measuring scores are: NMSE, ESNR, and PDR. The obtained results showed the dominant of the square wavelet thresholding method against other methods using different measuring scores and hence the conclusion by the way for adopting this proposed novel wavelet thresholding method for 1D signal compression in future researches.展开更多
第五代移动通信技术(5th-generation mobile communication technology,5G)网络对高速率、低时延、高可靠性的移动通信处理需求不断增加,对终端基带信道估计算法的高性能和低复杂度设计、矩阵处理动态范围提出挑战。针对上述问题,本文...第五代移动通信技术(5th-generation mobile communication technology,5G)网络对高速率、低时延、高可靠性的移动通信处理需求不断增加,对终端基带信道估计算法的高性能和低复杂度设计、矩阵处理动态范围提出挑战。针对上述问题,本文提出一种基于相关矩阵托普利兹(Toeplitz)特性的信道估计算法。依据信道的相干带宽特性计算信道相关矩阵并保留必要的较低矩阵阶数;基于相关矩阵的Toeplitz特性设计低复杂度的递归求逆算法,并针对加权矩阵乘法的元素重复性将矩阵乘法化简为矩阵点乘,简化加权矩阵运算;同时引入跟踪信噪比变化的缩放补偿因子对计算过程和结果分别进行缩放和补偿。理论分析和仿真结果显示,本文所提算法可在达到优异的信道估计性能条件下,有效降低运算复杂度,并极大降低算法矩阵处理的动态范围。展开更多
针对正交时频空(Orthogonal Time Frequency Space, OTFS)调制系统中均衡器性能不佳及线性滤波器复杂度较高等问题,提出了一种LU(Lower-Upper)分解与迭代最小均方误差(Iterative Minimum Mean Square Error, IMMSE)均衡器结合的OTFS系...针对正交时频空(Orthogonal Time Frequency Space, OTFS)调制系统中均衡器性能不佳及线性滤波器复杂度较高等问题,提出了一种LU(Lower-Upper)分解与迭代最小均方误差(Iterative Minimum Mean Square Error, IMMSE)均衡器结合的OTFS系统信号检测算法(LU-IMMSE)。该算法依据时延多普勒域稀疏信道矩阵的特征,采用一种低复杂度的LU分解方法,以避免MMSE均衡器求解矩阵逆的过程,在保证均衡器性能的前提下降低了均衡器复杂度。在OTFS系统中引入一种IMMSE均衡器,通过不断迭代更新发送符号均值和方差这些先验信息来逼近MMSE均衡器最优估计值。LU-IMMSE算法通过调节迭代次数可以有效降低误比特率。在比特信噪比为8 dB时,5次迭代后的LU-IMMSE均衡器误比特率相比传统的MMSE均衡器降低了约11 dB。随着迭代次数的增大,较传统IMMSE算法降低了计算复杂度。在最大时延系数为4、符号数为16的情况下,与直接求逆相比,所提出的低复杂度LU分解方法降低了约91.72%的矩阵求逆计算复杂度。展开更多
基金The authors gratefully acknowledge the support of the National Natural Science Foundation of China(No.11574250).
文摘Underwater acoustic signal processing is one of the research hotspots in underwater acoustics.Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing.Owing to the complexity of marine environment and the particularity of underwater acoustic channel,noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing.In order to solve the dilemma,we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),minimum mean square variance criterion(MMSVC) and least mean square adaptive filter(LMSAF).This noise reduction technique,named CEEMDAN-MMSVC-LMSAF,has three main advantages:(i) as an improved algorithm of empirical mode decomposition(EMD) and ensemble EMD(EEMD),CEEMDAN can better suppress mode mixing,and can avoid selecting the number of decomposition in variational mode decomposition(VMD);(ii) MMSVC can identify noisy intrinsic mode function(IMF),and can avoid selecting thresholds of different permutation entropies;(iii) for noise reduction of noisy IMFs,LMSAF overcomes the selection of deco mposition number and basis function for wavelet noise reduction.Firstly,CEEMDAN decomposes the original signal into IMFs,which can be divided into noisy IMFs and real IMFs.Then,MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs.Finally,both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained.Compared with other noise reduction techniques,the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals,which has the better noise reduction effect and has practical application value.CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection,feature extraction,classification and recognition of underwater acoustic signals.
基金Projects(41204079,41504086)supported by the National Natural Science Foundation of ChinaProject(20160101281JC)supported by the Natural Science Foundation of Jilin Province,ChinaProjects(2016M590258,2015T80301)supported by the Postdoctoral Science Foundation of China
文摘Nano-volt magnetic resonance sounding(MRS) signals are sufficiently weak so that during the actual measurement, they are affected by environmental electromagnetic noise, leading to inaccuracy of the extracted characteristic parameters and hindering effective inverse interpretation. Considering the complexity and non-homogeneous spatial distribution of environmental noise and based on the theory of adaptive noise cancellation, a model system for noise cancellation using multi-reference coils was constructed to receive MRS signals. The feasibility of this system with theoretical calculation and experiments was analyzed and a modified sigmoid variable step size least mean square(SVSLMS) algorithm for noise cancellation was presented. The simulation results show that, the multi-reference coil method performs better than the single one on both signal-to-noise ratio(SNR) improvement and signal waveform optimization after filtering, under the condition of different noise correlations in the reference coils and primary detecting coils and different SNRs. In particular, when the noise correlation is poor and the SNR<0, the SNR can be improved by more than 8 dB after filtering with multi-reference coils. And the average fitting errors for initial amplitude and relaxation time are within 5%. Compared with the normalized least mean square(NLMS) algorithm and multichannel Wiener filter and processing field test data, the effectiveness of the proposed method is verified.
基金Supported by National Research Foundation of Singapore （NRF-CRP8-2011-03） and National Natural Science Foundation of China （61120106011, 61034007, 61203045, 61304045）
基金Supported by National Natural Science Foundation of China(10571036)the Key Discipline Development Program of Beijing Municipal Commission (XK100080537)
文摘离群点检测任务是指检测与正常数据在特征属性上存在显著差异的异常数据。大多数基于聚类的离群点检测方法主要从全局角度对数据集中的离群点进行检测,而对局部离群点的检测性能较弱。基于此,本文通过引入快速搜索和发现密度峰值方法改进K-means聚类算法,提出了一种名为KLOD(local outlier detection based on improved K-means and least-squares methods)的局部离群点检测方法,以实现对局部离群点的精确检测。首先,利用快速搜索和发现密度峰值方法计算数据点的局部密度和相对距离,并将二者相乘得到γ值。其次,将γ值降序排序,利用肘部法则选择γ值最大的k个数据点作为K-means聚类算法的初始聚类中心。然后,通过K-means聚类算法将数据集聚类成k个簇,计算数据点在每个维度上的目标函数值并进行升序排列。接着,确定数据点的每个维度的离散程度并选择适当的拟合函数和拟合点,通过最小二乘法对升序排列的每个簇的每1维目标函数值进行函数拟合并求导,以获取变化率。最后,结合信息熵,将每个数据点的每个维度目标函数值乘以相应的变化率进行加权,得到最终的异常得分,并将异常值得分较高的top-n个数据点视为离群点。通过人工数据集和UCI数据集,对KLOD、LOF和KNN方法在准确度上进行仿真实验对比。结果表明KLOD方法相较于KNN和LOF方法具有更高的准确度。本文提出的KLOD方法能够有效改善K-means聚类算法的聚类效果,并且在局部离群点检测方面具有较好的精度和性能。
文摘In this paper a square wavelet thresholding method is proposed and evaluated as compared to the other classical wavelet thresholding methods (like soft and hard). The main advantage of this work is to design and implement a new wavelet thresholding method and evaluate it against other classical wavelet thresholding methods and hence search for the optimal wavelet mother function among the wide families with a suitable level of decomposition and followed by a novel thresholding method among the existing methods. This optimized method will be used to shrink the wavelet coefficients and yield an adequate compressed pressure signal prior to transmit it. While a comparison evaluation analysis is established, A new proposed procedure is used to compress a synthetic signal and obtain the optimal results through minimization the signal memory size and its transmission bandwidth. There are different performance indices to establish the comparison and evaluation process for signal compression;but the most well-known measuring scores are: NMSE, ESNR, and PDR. The obtained results showed the dominant of the square wavelet thresholding method against other methods using different measuring scores and hence the conclusion by the way for adopting this proposed novel wavelet thresholding method for 1D signal compression in future researches.
文摘第五代移动通信技术(5th-generation mobile communication technology,5G)网络对高速率、低时延、高可靠性的移动通信处理需求不断增加,对终端基带信道估计算法的高性能和低复杂度设计、矩阵处理动态范围提出挑战。针对上述问题,本文提出一种基于相关矩阵托普利兹(Toeplitz)特性的信道估计算法。依据信道的相干带宽特性计算信道相关矩阵并保留必要的较低矩阵阶数;基于相关矩阵的Toeplitz特性设计低复杂度的递归求逆算法,并针对加权矩阵乘法的元素重复性将矩阵乘法化简为矩阵点乘,简化加权矩阵运算;同时引入跟踪信噪比变化的缩放补偿因子对计算过程和结果分别进行缩放和补偿。理论分析和仿真结果显示,本文所提算法可在达到优异的信道估计性能条件下,有效降低运算复杂度,并极大降低算法矩阵处理的动态范围。
文摘针对正交时频空(Orthogonal Time Frequency Space, OTFS)调制系统中均衡器性能不佳及线性滤波器复杂度较高等问题,提出了一种LU(Lower-Upper)分解与迭代最小均方误差(Iterative Minimum Mean Square Error, IMMSE)均衡器结合的OTFS系统信号检测算法(LU-IMMSE)。该算法依据时延多普勒域稀疏信道矩阵的特征,采用一种低复杂度的LU分解方法,以避免MMSE均衡器求解矩阵逆的过程,在保证均衡器性能的前提下降低了均衡器复杂度。在OTFS系统中引入一种IMMSE均衡器,通过不断迭代更新发送符号均值和方差这些先验信息来逼近MMSE均衡器最优估计值。LU-IMMSE算法通过调节迭代次数可以有效降低误比特率。在比特信噪比为8 dB时,5次迭代后的LU-IMMSE均衡器误比特率相比传统的MMSE均衡器降低了约11 dB。随着迭代次数的增大,较传统IMMSE算法降低了计算复杂度。在最大时延系数为4、符号数为16的情况下,与直接求逆相比,所提出的低复杂度LU分解方法降低了约91.72%的矩阵求逆计算复杂度。
