In this paper, a classification method based on Support Vector Machine (SVM) is given in the digital modulation signal classification. The second, fourth and sixth order cumulants of the received signals are used as c...In this paper, a classification method based on Support Vector Machine (SVM) is given in the digital modulation signal classification. The second, fourth and sixth order cumulants of the received signals are used as classification vectors firstly, then the kernel thought is used to map the feature vector to the high dimensional feature space and the optimum separating hyperplane is constructed in space to realize signal recognition. In order to build an effective and robust SVM classifier, the radial basis kernel function is selected, one against one or one against rest of multi-class classifier is designed, and method of parameter selection using cross- validation grid is adopted. Through the experiments it can be concluded that the classifier based on SVM has high performance and is more robust.展开更多
This paper mainly studies the data characteristics of high order cumulants using digitally modulated signals, and constructs the identification feature parameters that can distinguish the signal modulation type by the...This paper mainly studies the data characteristics of high order cumulants using digitally modulated signals, and constructs the identification feature parameters that can distinguish the signal modulation type by the high-order cumulants data of the digital modulation signal. Set the identification signal modulation type determination threshold based on the value of the identification feature parameter. The identification feature parameter value of the signal modulation type is compared with the set determination threshold, to realize the recognition of digital modulation signal. This identification method is implemented based on MATLAB design, with a 2ASK (2-ary Amplitude Shift Keying) signal, 4ASK (4-ary Amplitude Shift Keying) signal, 2PSK (2-ary Phase Shift Keying) signal, 4PSK (4-ary Phase Shift Keying) signal, 2FSK (2-ary Frequency Shift Keying) signal, 4FSK (4-ary Frequency Shift Keying) signal. The second, fourth and sixth order cumulants of the six signals were analyzed. Calculate the selected identification feature parameter value and the determination threshold to identify the six signals. The six signals have made MATLAB identification simulation. Simulation results show that this method is feasible and has high recognition rate. Simulation results verify that such recognition methods maintain a high recognition rate under conditions with low signal-to-noise ratio. This identification method can be extended to more MASK (M-ary Amplitude Shift Keying), MPSK (M-ary Phase Shift Keying), MFSK (M-ary Frequency Shift Keying), MQAM (M-ary Quadrature Amplitude Modulation) signal identification.展开更多
This paper gives a MUSIC signal DOA estimation algorithm based on the modified high-order cumulant matrix which is constructed by the recieved data and their conjugate rearrangements. When the snapshot number is limit...This paper gives a MUSIC signal DOA estimation algorithm based on the modified high-order cumulant matrix which is constructed by the recieved data and their conjugate rearrangements. When the snapshot number is limited, this algorithm can improve the signal DOA estimation performances obviously, and its computational complexity scarcely increases. Finally, some simulation results to verify the theoretical analyses are presented.展开更多
Spectral estimation method is an effective method in the case of parameter estimation of sinusoid signal in noises.It always applies cross spectral estimation theory and high order self spectral estimation methods up ...Spectral estimation method is an effective method in the case of parameter estimation of sinusoid signal in noises.It always applies cross spectral estimation theory and high order self spectral estimation methods up to now.Yule Walker equation based on cross fourth order cumulant is developed and Pisarenko method is provided in this paper.Simulation results show that the method is effective to retrieve sinusoid signal frequencies from hybrid colored noises and has good performance.展开更多
Traditional approaches of spatial spectral estimation are usually based on the second-order statistics. The higher-order cumulants and the poly-spectrum contain more information and are capable of reducing the Gaussia...Traditional approaches of spatial spectral estimation are usually based on the second-order statistics. The higher-order cumulants and the poly-spectrum contain more information and are capable of reducing the Gaussian noise. In this paper, we present a new spectrum estimation method for direction-finding, the FOMUSIC algorithm, which is based on the eigen-structure analysis of the fourth-order cumulants. The derivation of the algorithm is given in detail and its performance is illustrated by both the computer simulations and the experiments of a direction-finding system. The obtained results demonstrate that the fourth-order cumulants based method outperforms the traditional methods, especially when the noise is an unknown colored one.展开更多
局部放电是电力变压器绝缘劣化的主要原因,研究局部放电定位对提高电网的安全运行很有帮助。该文研制了一种用于电力变压器局部放电定位的复合传感器,并针对该传感器进行了定位仿真和实验。复合传感器由共形的13阵元十字形超声波传感器...局部放电是电力变压器绝缘劣化的主要原因,研究局部放电定位对提高电网的安全运行很有帮助。该文研制了一种用于电力变压器局部放电定位的复合传感器,并针对该传感器进行了定位仿真和实验。复合传感器由共形的13阵元十字形超声波传感器阵列和22阵元的超高频传感器阵列组成。应用高阶累积量处理技术对十字形超声阵列进行虚拟扩展,扩展后阵列具有61个阵元的阵列性能,提高了超声阵列的孔径和方向性锐度,这极大减少了后续硬件电路和成本。利用扩展超声阵列配合超高频阵列来仿真局部放电定位,结果表明扩展阵列具有很好的定位效果。在噪声背景下与多重信号分类(multiple signal classification,MUSIC)方法作了比较,结果表明高阶累积量处理技术能更好地抑制各种高斯色噪声的干扰。基于该十字形超声波阵列传感器进行了局部放电定位实验,结果表明扩展后的十字形超声阵列能精确地定位局部放电,定位的相对误差小于5%。对较少数目阵元的阵列实施虚拟扩展技术,为阵列技术在电力设备上的实用化提供了可能性。展开更多
文摘In this paper, a classification method based on Support Vector Machine (SVM) is given in the digital modulation signal classification. The second, fourth and sixth order cumulants of the received signals are used as classification vectors firstly, then the kernel thought is used to map the feature vector to the high dimensional feature space and the optimum separating hyperplane is constructed in space to realize signal recognition. In order to build an effective and robust SVM classifier, the radial basis kernel function is selected, one against one or one against rest of multi-class classifier is designed, and method of parameter selection using cross- validation grid is adopted. Through the experiments it can be concluded that the classifier based on SVM has high performance and is more robust.
文摘This paper mainly studies the data characteristics of high order cumulants using digitally modulated signals, and constructs the identification feature parameters that can distinguish the signal modulation type by the high-order cumulants data of the digital modulation signal. Set the identification signal modulation type determination threshold based on the value of the identification feature parameter. The identification feature parameter value of the signal modulation type is compared with the set determination threshold, to realize the recognition of digital modulation signal. This identification method is implemented based on MATLAB design, with a 2ASK (2-ary Amplitude Shift Keying) signal, 4ASK (4-ary Amplitude Shift Keying) signal, 2PSK (2-ary Phase Shift Keying) signal, 4PSK (4-ary Phase Shift Keying) signal, 2FSK (2-ary Frequency Shift Keying) signal, 4FSK (4-ary Frequency Shift Keying) signal. The second, fourth and sixth order cumulants of the six signals were analyzed. Calculate the selected identification feature parameter value and the determination threshold to identify the six signals. The six signals have made MATLAB identification simulation. Simulation results show that this method is feasible and has high recognition rate. Simulation results verify that such recognition methods maintain a high recognition rate under conditions with low signal-to-noise ratio. This identification method can be extended to more MASK (M-ary Amplitude Shift Keying), MPSK (M-ary Phase Shift Keying), MFSK (M-ary Frequency Shift Keying), MQAM (M-ary Quadrature Amplitude Modulation) signal identification.
文摘This paper gives a MUSIC signal DOA estimation algorithm based on the modified high-order cumulant matrix which is constructed by the recieved data and their conjugate rearrangements. When the snapshot number is limited, this algorithm can improve the signal DOA estimation performances obviously, and its computational complexity scarcely increases. Finally, some simulation results to verify the theoretical analyses are presented.
文摘Spectral estimation method is an effective method in the case of parameter estimation of sinusoid signal in noises.It always applies cross spectral estimation theory and high order self spectral estimation methods up to now.Yule Walker equation based on cross fourth order cumulant is developed and Pisarenko method is provided in this paper.Simulation results show that the method is effective to retrieve sinusoid signal frequencies from hybrid colored noises and has good performance.
文摘Traditional approaches of spatial spectral estimation are usually based on the second-order statistics. The higher-order cumulants and the poly-spectrum contain more information and are capable of reducing the Gaussian noise. In this paper, we present a new spectrum estimation method for direction-finding, the FOMUSIC algorithm, which is based on the eigen-structure analysis of the fourth-order cumulants. The derivation of the algorithm is given in detail and its performance is illustrated by both the computer simulations and the experiments of a direction-finding system. The obtained results demonstrate that the fourth-order cumulants based method outperforms the traditional methods, especially when the noise is an unknown colored one.
文摘局部放电是电力变压器绝缘劣化的主要原因,研究局部放电定位对提高电网的安全运行很有帮助。该文研制了一种用于电力变压器局部放电定位的复合传感器,并针对该传感器进行了定位仿真和实验。复合传感器由共形的13阵元十字形超声波传感器阵列和22阵元的超高频传感器阵列组成。应用高阶累积量处理技术对十字形超声阵列进行虚拟扩展,扩展后阵列具有61个阵元的阵列性能,提高了超声阵列的孔径和方向性锐度,这极大减少了后续硬件电路和成本。利用扩展超声阵列配合超高频阵列来仿真局部放电定位,结果表明扩展阵列具有很好的定位效果。在噪声背景下与多重信号分类(multiple signal classification,MUSIC)方法作了比较,结果表明高阶累积量处理技术能更好地抑制各种高斯色噪声的干扰。基于该十字形超声波阵列传感器进行了局部放电定位实验,结果表明扩展后的十字形超声阵列能精确地定位局部放电,定位的相对误差小于5%。对较少数目阵元的阵列实施虚拟扩展技术,为阵列技术在电力设备上的实用化提供了可能性。
