FIELD: hydroacoustics.
SUBSTANCE: invention relates to hydroacoustics and can be used to build intelligent automated classification systems for marine targets detected by signs of amplitude-phase modulation of low-frequency signals of pumping the marine environment by radiation and fields of objects. Essence: the system for detecting and classifying marine targets based on neural network technologies and artificial intelligence elements contains a working zone of nonlinear interaction and parametric transformation of pumping waves and information waves formed in the marine environment. The length of the working area is equal to the length of the controlled area of the marine environment, for which the emitting and receiving converters are located on opposite boundaries of the site. The input of the emitting converter is connected by an underwater cable to the output of the pump signal emission path, which contains a series-connected pump signal generator of stabilized frequency, a power amplifier and a unit for matching its output with an underwater cable. The output of the receiving converter is connected by an underwater cable to the input of the path for receiving, processing and recording information signals, which contains a series-connected broadband amplifier, a time-frequency converter, a spectrum analyzer and a functionally connected recorder. At the same time, the output of the spectrum analyzer of the information signal reception, processing and registration path is connected to the input of the target class recognition unit according to the amplitude-frequency characteristics of the neural network recognition and classification path covered by feedback from the training unit. Additionally, a synthesis path of neural network and neuro-fuzzy recognition models with grouping of features is introduced, containing a unit for analyzing information about the features and topology of the training sample, the input of which is connected to the output of the training unit of the neural network recognition and classification path, and the output is connected to the input of the neural network synthesis block, covered by feedback with the synthesis block of neuro-fuzzy networks, the output of which is connected to the input of a logical device, the function of which is performed by a six-layer neuro-fuzzy Wang-Mendel network, where neural and neuro-fuzzy models are synthesized in a non-iterative mode with linearization, factor grouping and convolution of features of a marine target and a fuzzy logical conclusion is formed for the training unit of the neural network recognition and classification path, which provides the final classification solution for detected marine targets, according to the degree of belonging of the studied spectral region to the classification object.
EFFECT: automation of the process of recognizing classes of marine targets (surface or underwater object), increasing the probability of reliable classification of a marine target by 5-7% more than when using a prototype.
1 cl, 11 dwg
