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Signal data fusion method and identification method

A technology of signal data and fusion method, applied in character and pattern recognition, neural learning methods, instruments, etc., can solve problems such as inability to process sequential data, and achieve improved extraction and recognition effects

Pending Publication Date: 2022-03-18
SHANGHAI INST OF MICROSYSTEM & INFORMATION TECH CHINESE ACAD OF SCI
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Problems solved by technology

However, CNN is generally used to process matrix data and cannot process sequence data.

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  • Signal data fusion method and identification method
  • Signal data fusion method and identification method

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Embodiment Construction

[0031] Below in conjunction with specific embodiment, further illustrate the present invention. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the teachings of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of the present application.

[0032] The first embodiment of the present invention relates to a signal data fusion method, such as figure 1 As shown, the following steps are included: obtaining the data of m sensors at the time t~t+h; using adaptive empirical mode decomposition to decompose the data of the m sensors at the time t~t+h to obtain the The eigenmode function and the corresponding residual value; the eigenmode function of each sensor data at the...

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Abstract

The invention relates to a signal data fusion method and identification method, and the fusion method comprises the following steps: obtaining the data of m sensors at t-t + h moments; adopting adaptive empirical mode decomposition to decompose the data of the m sensors at the moment of t-t + h to obtain eigenmode functions of all the data and corresponding residual values; extracting and fusing the eigenmode functions of the sensor data at the t-t + h moments according to the moments to obtain a mode decomposition data matrix of the sensor data at the t-t + h moments; and stacking the obtained modal decomposition data matrixes of the sensor data at the time of t-t + h to obtain the modal decomposition data matrixes of all the data at the time of t-t + h. According to the method, more data feature details can be displayed while the one-dimensional data is converted into the matrix data.

Description

technical field [0001] The invention relates to the technical field of wireless communication, in particular to a signal data fusion method and identification method. Background technique [0002] In wireless communication cognitive radio, spectrum feature recognition is an important content to realize dynamic spectrum management. In traditional spectral feature recognition, Fourier transform, wavelet transform, Hilbert transform, etc. are usually used to convert time-domain information into spectral information, so as to reveal the characteristics of the signal. However, during the conversion process of Fourier transform and wavelet transform, the characteristics of the time dimension of the signal are lost. Although the time domain information of the signal can be preserved to the greatest extent by using the Hilbert transform, the processing of the signal is one-dimensional. This one-dimensional sequential signal processing method loses the structural relationship infor...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/25
Inventor 刘远庆柳军王文彬刘建坡甘述绍
Owner SHANGHAI INST OF MICROSYSTEM & INFORMATION TECH CHINESE ACAD OF SCI