In underwater target detection, the bottom reverberation has some of the same properties as the target echo, which has a great impact on the performance. It is essential to study the difference between target echo and...In underwater target detection, the bottom reverberation has some of the same properties as the target echo, which has a great impact on the performance. It is essential to study the difference between target echo and reverberation. In this paper, based on the unique advantage of human listening ability on objects distinction, the Gammatone filter is taken as the auditory model. In addition, time-frequency perception features and auditory spectral features are extracted for active sonar target echo and bottom reverberation separation. The features of the experimental data have good concentration characteristics in the same class and have a large amount of differences between different classes, which shows that this method can effectively distinguish between the target echo and reverberation.展开更多
In order to solve the difficulty of detailed recognition of subdivisions of structural coal types,a differentiation model that combines BP neural network with an ultrasonic reflection method is proposed.Structural coa...In order to solve the difficulty of detailed recognition of subdivisions of structural coal types,a differentiation model that combines BP neural network with an ultrasonic reflection method is proposed.Structural coal types are recognized based on a suitable consideration of ultrasonic speed,an ultrasonic attenuation coefficient,characteristics of ultrasonic transmission and other parameters relating to structural coal types.We have focused on a computational model of ultrasonic speed,attenuation coefficient in coal and differentiation algorithm of structural coal types based on a BP neural network.Experiments demonstrate that the model can distinguish structural coal types effectively.It is important for the improved ultrasonic differentiation model to predict coal and gas outbursts.展开更多
The problem caused by shortness or excessiveness of snapshots and by coherent sources in underwater acoustic positioning is considered.A matched field localization algorithm based on CS-MUSIC(Compressive Sensing Multi...The problem caused by shortness or excessiveness of snapshots and by coherent sources in underwater acoustic positioning is considered.A matched field localization algorithm based on CS-MUSIC(Compressive Sensing Multiple Signal Classification) is proposed based on the sparse mathematical model of the underwater positioning.The signal matrix is calculated through the SVD(Singular Value Decomposition) of the observation matrix.The observation matrix in the sparse mathematical model is replaced by the signal matrix,and a new concise sparse mathematical model is obtained,which means not only the scale of the localization problem but also the noise level is reduced;then the new sparse mathematical model is solved by the CS-MUSIC algorithm which is a combination of CS(Compressive Sensing) method and MUSIC(Multiple Signal Classification) method.The algorithm proposed in this paper can overcome effectively the difficulties caused by correlated sources and shortness of snapshots,and it can also reduce the time complexity and noise level of the localization problem by using the SVD of the observation matrix when the number of snapshots is large,which will be proved in this paper.展开更多
基金the National Natural Science Foundation of China
文摘In underwater target detection, the bottom reverberation has some of the same properties as the target echo, which has a great impact on the performance. It is essential to study the difference between target echo and reverberation. In this paper, based on the unique advantage of human listening ability on objects distinction, the Gammatone filter is taken as the auditory model. In addition, time-frequency perception features and auditory spectral features are extracted for active sonar target echo and bottom reverberation separation. The features of the experimental data have good concentration characteristics in the same class and have a large amount of differences between different classes, which shows that this method can effectively distinguish between the target echo and reverberation.
基金Projects 50674093 supported by the National Natural Science Foundation of China20050290010 by the Doctoral Foundation of the Chinese Education Ministry
文摘In order to solve the difficulty of detailed recognition of subdivisions of structural coal types,a differentiation model that combines BP neural network with an ultrasonic reflection method is proposed.Structural coal types are recognized based on a suitable consideration of ultrasonic speed,an ultrasonic attenuation coefficient,characteristics of ultrasonic transmission and other parameters relating to structural coal types.We have focused on a computational model of ultrasonic speed,attenuation coefficient in coal and differentiation algorithm of structural coal types based on a BP neural network.Experiments demonstrate that the model can distinguish structural coal types effectively.It is important for the improved ultrasonic differentiation model to predict coal and gas outbursts.
基金supported by the National Natural Science Foundation of China (61202208)
文摘The problem caused by shortness or excessiveness of snapshots and by coherent sources in underwater acoustic positioning is considered.A matched field localization algorithm based on CS-MUSIC(Compressive Sensing Multiple Signal Classification) is proposed based on the sparse mathematical model of the underwater positioning.The signal matrix is calculated through the SVD(Singular Value Decomposition) of the observation matrix.The observation matrix in the sparse mathematical model is replaced by the signal matrix,and a new concise sparse mathematical model is obtained,which means not only the scale of the localization problem but also the noise level is reduced;then the new sparse mathematical model is solved by the CS-MUSIC algorithm which is a combination of CS(Compressive Sensing) method and MUSIC(Multiple Signal Classification) method.The algorithm proposed in this paper can overcome effectively the difficulties caused by correlated sources and shortness of snapshots,and it can also reduce the time complexity and noise level of the localization problem by using the SVD of the observation matrix when the number of snapshots is large,which will be proved in this paper.
