FIELD: hydroacoustics.
SUBSTANCE: invention relates to hydroacoustics and can be used to implement neural network recognition operations of target classes (surface or underwater object) detected by signs of amplitude-phase modulation of low-frequency signals of pumping the marine environment by radiation and fields of objects. Essence: in the method for detecting and classifying marine targets based on neural network technologies and artificial intelligence elements, when the device is turned on in the marine environment, a zone of nonlinear interaction and parametric transformation of pumping waves with object signals is formed, for which the emitter and the receiving antenna are placed on opposite borders of the controlled area of the marine environment, then the pumping waves modulated by the object signals are received and amplified in the parametric transformation band, their frequency-time scale is transferred to the high-frequency region, a narrow-band spectral analysis is carried out, parametric components of the total or difference frequency are isolated, according to which, taking into account the time and parametric transformation of waves, the characteristics of the object’s signals are restored, which are fed into the neural network recognition and classification path, consisting of a target class recognition unit based on amplitude-frequency characteristics, implementing computational operations of an artificial neural network and covered by feedback with the training unit, in whose memory the data of mathematically processed images of spectrograms of marine targets are recorded, moreover, the first input of the target class recognition unit according to amplitude-frequency characteristics receives data from the output of the spectrum analyzer of the signal reception, processing and registration path, and its second input receives data from the training unit of the neural network recognition and classification path. In this case, the data is fed into the additionally introduced synthesis path of neural network and neural fuzzy recognition models with grouping of features at the input, the unit for analyzing information about the features and topology of the training sample, and then fed to the input of the neural network synthesis unit, covered by feedback with the neural fuzzy network synthesis unit, the output of which is connected to the input of a logical device, the function of which is performed by a six-layer Wang-Mendel neural fuzzy network, where neural and neural fuzzy models are synthesized in a non-iterative mode with linearization, factor grouping and convolution of signs of a marine target and a fuzzy logical conclusion is formed for the training unit of the neural network recognition and classification path, after which an artificial neural network is configured and a conclusion is formed about the degree of belonging of the studied spectral region to the classification object (surface or underwater object).
EFFECT: an increase in the efficiency of classification of marine targets by 5-7% relative to the prototype and other classification methods using modulation of low-frequency signals of pumping the marine environment by radiation and fields of objects.
1 cl, 11 dwg
