НЕЙРОСЕТЕВОЙ АЛГОРИТМ ДЕТЕКТИРОВАНИЯ СИГНАЛОВ С ДВОИЧНОЙ ПОЗИЦИОННО-ИМПУЛЬСНОЙ МОДУЛЯЦИЕЙ

Abstract

In the work, a neural network method for detecting signals with binary position-pulse modulation was studied. The detection process was reduced to distinguishing signals by a neural network, the architecture of which included convolutional and fully-connected layers. The network was trained on the basis of a set of labeled realizations of a sum of signals with noise, which were represented by discrete samples. The TensorFlow library was used to model and train the network. A bell video pulse and a quasi-radio signal with a bell modulating function were used as possible signal models. The functioning efficiency of the trained neural network was studied by the methods of statistical modeling on a computer. The detection quality of the neural network algorithm is compared with the classical one synthesized in the framework of the statistical decision theory. The possibility of training a neural network detector with an unknown duration of the received signals is investigated. The efficiency of the neural network detector is compared with the quasi-optimal one, which does not take into account the possible unknown of the signal duration. It is shown that the neural network detection algorithm is significantly inferior to the optimal one synthesized by the maximum likelihood method. However, in the presence of a priori uncertainty about the signal duration, the quasi-optimal detector is inferior in efficiency to the neural network one. Recommendations are formulated for a reasonable choice of the detector, depending on the required quality of its work.