Mixed source positioning method based on convolutional neural network

A convolutional neural network and source localization technology, which is used in the field of simultaneous localization of near-field and far-field sources using radar arrays, which can solve problems such as inability to locate near-field sources or mixed sources, and achieve faster convergence and larger arrays. Aperture, effect of reducing estimation time

Active Publication Date: 2021-04-23
NAT UNIV OF DEFENSE TECH
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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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  • Mixed source positioning method based on convolutional neural network
  • Mixed source positioning method based on convolutional neural network
  • Mixed source positioning method based on convolutional neural network

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Embodiment Construction

[0032] The present invention will be further described below in conjunction with accompanying drawings and examples.

[0033] The present invention comprises the following steps:

[0034] First, use the radar antenna array to obtain the mixed source phase difference matrix; then, input the information of the mixed source phase difference matrix into the first convolutional neural network to calculate the direction of arrival of the mixed source; secondly, use the output of the first convolutional neural network Information, remove the direction of arrival parameter contained in the phase difference matrix information of the mixed source, and input it to the autoencoder; finally, input the output of the autoencoder to the second convolutional neural network to identify and determine the mixed source The distance to the near-field source.

[0035] Such as figure 1 As shown, the hybrid source localization method based on convolutional neural network includes the following steps...

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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...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/08G01S5/02G01S13/06
Inventor 刘振苏晓龙刘天鹏户盼鹤彭勃刘永祥黎湘
Owner NAT UNIV OF DEFENSE TECH
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