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Near-field source positioning method based on deep learning

A technology of deep learning and positioning method, which is applied in the field of array signal processing and artificial intelligence, and can solve problems such as large amount of calculation and poor generalization ability of parameter estimation

Active Publication Date: 2021-04-23
NAT UNIV OF DEFENSE TECH
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Problems solved by technology

However, compared with the method based on deep learning, this method has a large amount of calculation, and the generalization ability of parameter estimation is poor, that is, when the direction of arrival and distance parameters are not on the grid divided by the MUSIC method, this method will appear large error

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  • Near-field source positioning method based on deep learning
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  • Near-field source positioning method based on deep learning

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Embodiment Construction

[0035] The present invention will be further described below in conjunction with accompanying drawings and examples.

[0036] The present invention comprises the following steps:

[0037] First, the radar antenna array is used to obtain the phase difference matrix of near-field sources; then, the information of the phase difference matrix of near-field sources is input into the autoencoder to calculate the direction of arrival of near-field sources; secondly, the output of the autoencoder is input into the first One type of convolutional neural network is used to calculate the direction of arrival of near-field sources; finally, using the output information of the first type of convolutional neural network, the parameters of the direction of arrival contained in the phase difference matrix information of near-field sources are removed, and input to the second A class II convolutional neural network that computes distances to near-field sources.

[0038] Such as figure 1 As s...

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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...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08G06F17/16G01S13/06
Inventor 刘振苏晓龙户盼鹤彭勃刘天鹏刘永祥黎湘
Owner NAT UNIV OF DEFENSE TECH
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