Mixed source positioning method based on convolutional neural network

Abstract

The invention provides a mixed source positioning method based on a convolutional neural network. According to the technical scheme, the method comprises the following steps: firstly, obtaining a mixed source phase difference matrix by utilizing a radar antenna array; then, inputting the information of the mixed source phase difference matrix into a first convolutional neural network, and calculating the direction of arrival of the mixed source; secondly, removing direction-of-arrival parameters contained in the mixed source phase difference matrix information by utilizing output information of a first convolutional neural network, and inputting the direction-of-arrival parameters into an automatic encoder; and finally, inputting the output of the automatic encoder into the second convolutional neural network, identifying the mixed source and determining the distance of the near-field source. The method can achieve the simultaneous positioning of the near-field source and the far-field source, is high in convergence speed during the training of the provided convolutional neural network, is short in time for calculating the positioning parameters during the use, is high in positioning precision, and is strong in generalization capability.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence and array signal processing, in particular to a method for simultaneously locating near-field sources and far-field sources by using a radar array. Background technique [0002] Mixed source localization plays an important role in passive radar. The mixed source includes near-field sources and far-field sources. The distance between the near-field source and the radar array is usually 0.62(D 3 / λ) 1 / 2 ~2D 2 / λ, where D is the aperture of the radar array, and λ is the wavelength of the radar receiving signal. To locate the near-field source needs to estimate the direction of arrival (Direction Of Arrival, DOA) and distance; the distance of the far-field source is usually relative to the radar array greater than 2D 2 / λ, the location of the far-field source needs to estimate the direction of arrival. Convolutional Neural Networks (CNN) is a feedforward neural network with convolu...

AI Technical Summary

Problems solved by technology

However, this method can only realize the direction of arrival estimation of far-field sources, and cannot locate near-field sources or mixed sources.

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