Feature extraction method of underwater target based on convolutional neural network
A convolutional neural network and underwater target technology, applied in the field of underwater target feature extraction, can solve problems such as displacement, loss of spatial information, and deformation of observed targets
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[0090] The following examples describe the present invention in more detail.
[0091] The time-frequency domain conversion is performed on the original noise signal to generate a LoFAR spectrogram that can represent time-frequency domain information. The specific processing process is:
[0092] 1. Define S(n) as the sampling sequence of the original radiation noise signal, divide it into 25 continuous parts, and set 25 sampling points for each part. Among them, 25 consecutive parts are allowed to have overlapping parts of data, and the degree of crossover is set to 50%.
[0093] 2. Define M j (n) is the sampling sample of the j-th segment signal, and it is normalized and centered, the purpose of which is to make the amplitude of the radiation noise signal evenly distributed in time and achieve DC removal so that the mean value of the sample is zero.
[0094] Normalization processing:
[0095]
[0096] In order to facilitate the calculation of the Fourier transform, the ...
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