Development of a hybrid neural network model for mine detection by using ultrawideband radar data

Abstract

The object of the study is the architecture of a hybrid neural network for mine recognition using ultra-wideband radar data. The work solves the problem of filtering reflected signals with interference and recognizing mines detected by ultra-wideband (UWB) radar. A hybrid neural network model in combination with the Adam learning algorithm is proposed. Filtering of reflected signals from mines is carried out using an MLP (multilayer perceptron) filter, which selects low-amplitude parts of signals that carry information about a hidden mine from the entire reflected signal. Mine recognition is carried out by a Hilbert block and an oscillatory neural network, which are included in the structure of a hybrid neural network. The peculiarity of the obtained results, which allowed to solve the investigated problem, is the transformation of the signal frequency by the Hilbert block and the recognition of mines by the oscillatory neural network in the resonant mode. The three-layer MLP filter effectively filters out the unwanted component in the total signal reflected from the subsurface object, as the MSE (Mean Squared Error) of the MLP filter is 1·10-5. If the frequency of the Hilbert signal is equal to the natural frequency of oscillations of neurons then the recognition of signals with a small amplitude from subsurface objects is carried out by an oscillatory neural network based on the resonant amplitude, which is indicated by a small value of cross-entropy. The proposed model of a hybrid neural network provides amplification of useful signals due to resonance and has higher performance compared to existing models of artificial neural networks. The practical significance of the obtained results lies in their application in the field of automated neural network technologies for detection and recognition of subsurface objects of various nature based on reflected radar signals with an amplitude at the noise level