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Radar radiation source deep learning identification method based on non-fingerprint signal eliminator

A fingerprint signal and deep learning technology, applied in the field of deep learning recognition of radar radiation sources, can solve the problems of not being clearly distinguished and decoupled, the impact of final recognition accuracy, and signal mixing, so as to improve recognition accuracy and stability, High radiation source identification accuracy and stability, and the effect of improving identification accuracy

Active Publication Date: 2020-05-08
ZHEJIANG UNIV
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Problems solved by technology

[0003] However, in previous studies, the fingerprint (fingerprint features) and non-fingerprint (un-fingerprint features) features of the signal have not been clearly distinguished and decoupled, which makes the two kinds of signals mixed together in the process of identification, which has a negative effect on The accuracy of the final recognition has a greater impact on the

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  • Radar radiation source deep learning identification method based on non-fingerprint signal eliminator
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  • Radar radiation source deep learning identification method based on non-fingerprint signal eliminator

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Embodiment Construction

[0040] The present invention will be described in detail below according to the accompanying drawings.

[0041] 1) Signal preprocessing;

[0042] Depend on figure 1, the fixed network input picture specification is 224 pixels*224 pixels*3 channels, if the number of sampling points of the radiation source signal a≥224 2 =50176, then take continuous 50176 sampling point data and fill them into the matrix M∈R row by row in order 224×224 , and then copy it to get the matrix I∈R 224×224×3 , to complete the data preprocessing; if the number of sampling points a<50176, fill in 0 after the sequence until it reaches a certain square number, fill it to a square matrix M of a certain size, and then use the interpolation algorithm to enlarge it to a size of 224×224, Then copy to get the input image I.

[0043] 2) Data set division;

[0044] In the training process of deep learning convolutional neural network, the data needs to be divided into training set, verification set and test ...

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Abstract

The invention discloses a radar radiation source deep learning recognition method based on a non-fingerprint signal eliminator, an original radar radiation source signal comprises a fingerprint feature part and a non-fingerprint feature part, and the recognition accuracy of a radar radiation source can be improved to a great extent through extraction of fingerprint features and suppression and elimination of non-fingerprint features. According to the invention, the deep learning network is used as the radiation source signal feature extractor, and the proposed non-fingerprint signal eliminatoris combined to extract the fingerprint information of the radiation source signal, eliminate and suppress the non-fingerprint signal, and improve the radar radiation source recognition effect.

Description

technical field [0001] The invention relates to the field of radar radiation source identification, in particular to a radar radiation source deep learning identification method based on a non-fingerprint signal canceller. Background technique [0002] Radar emitter identification (Specific Emitter Identification) is one of the main functions of radar countermeasure system, which has important strategic and tactical significance. Machine learning, especially deep learning, has been widely used to solve the problem of radar radiation source identification, and achieved high recognition accuracy. The internal characteristics of radar emitter emission signals have received extensive attention in the field of emitter identification in recent years. Special emitter identification means the ability to attach unique electromagnetic properties to specific emitters. These characteristics belong to intra-pulse modulation characteristics (intra-pulse modulation), including intentiona...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08G01S7/02
CPCG06N3/08G01S7/021G06N3/047G06N3/045G06F2218/08G06F2218/12G06F18/241G06F18/2415
Inventor 吕以豪仵志鹏张志猛茆旋宇王文海王欢张泽银陈歆伟闫正兵刘兴高
Owner ZHEJIANG UNIV
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