Near-field source positioning method based on deep learning

Abstract

The invention provides a near-field source positioning method based on deep learning. According to the technical scheme, the method comprises the following steps: firstly, obtaining a near-field source phase difference matrix by utilizing a radar antenna array; then, inputting the information of the near-field source phase difference matrix into an automatic encoder, and calculating the direction of arrival of the near-field source; secondly, the output of the automatic encoder is input into a first-class convolutional neural network, and the direction of arrival of a near-field source is calculated; and finally, removing direction-of-arrival parameters contained in the near-field source phase difference matrix information by utilizing output information of the first type of convolutional neural network, inputting the direction-of-arrival parameters into the second type of convolutional neural network, and calculating the distance of the near-field source. The direction of arrival and distance parameters of multiple near-field sources can be effectively separated and calculated, the positioning precision is high, and the generalization ability is high.

Description

technical field

[0001] The invention belongs to the technical field of array signal processing and artificial intelligence, in particular to a method for locating near-field sources by using a radar array. Background technique

[0002] Near-field source location plays an important role in passive radar and anti-radiation missile strikes. According to the distance between the radiation source and the radar array, radiation source location includes far-field source location and near-field source location. Compared with far-field source location, it only needs to estimate the direction of arrival (Direction Of Arriva, DOA). Localization requires estimation of DOA and distance parameters, and near-field sources are usually at a distance of 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. The use of deep learning for information source location is driven by data to establish a nonlinear mapping relat...

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