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Unknown target identification method based on deep convolutional neural network

A deep convolution and neural network technology, applied in the field of unknown target recognition based on deep convolutional neural network, can solve the problem of inability to recognize unknown targets

Active Publication Date: 2020-06-30
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

Based on the conventional deep convolutional neural network, the method uses the training one-dimensional range image data of the known target to obtain the recognition threshold, effectively describes the statistical distribution area boundary of the known target and the unknown target data set, and realizes the recognition of the unknown target. Recognition, which solves the problem that conventional neural networks cannot recognize unknown targets

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  • Unknown target identification method based on deep convolutional neural network
  • Unknown target identification method based on deep convolutional neural network
  • Unknown target identification method based on deep convolutional neural network

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Embodiment Construction

[0024] The validity of the present invention is proved below in combination with a simulation example.

[0025] Experiments are carried out using the simulated one-dimensional range images of five different types of military aircraft, AH64, AN26, F15, B1B, and B52, obtained from the special electromagnetic simulation characteristic scene. The experimental simulation radar parameters include: the radar carrier frequency is 6GHz, and the radar bandwidth is 400MHz. In the simulation scene, the simulation target collects a one-dimensional range image every 0.1° within the azimuth angle of 0°-180° at an elevation angle of 3°, and collects 1801 one-dimensional range images for each type of aircraft, and each one-dimensional range image Each contains 320 range units, that is, the input data of each type of aircraft is a one-dimensional range image matrix of 1801×320.

[0026] In the process of training and updating parameters W, the random initialization weight W=[w 1 ,w 2 ,w 3 ]...

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Abstract

The invention belongs to the technical field of unknown target recognition, and particularly relates to an unknown target recognition method based on a deep convolutional neural network. The method comprises the following steps: firstly, preprocessing one-dimensional range profile data (HRRP) obtained by a broadband radar to reduce the amplitude sensitivity of a one-dimensional range profile; secondly, extracting features by using a deep convolutional neural network; and finally, processing the recognition probability of the known target data through a difference probability method, obtaininga discrimination threshold, and discriminating the output vector of the neural network, thereby recognizing an unknown target. According to the method, the discrimination threshold acquired by adopting the difference probability method is introduced, so that the statistical distribution region boundary of the known target and unknown target data sets is effectively described, and the problem thatthe conventional convolutional neural network cannot identify the unknown target is solved.

Description

technical field [0001] The invention belongs to the technical field of unknown target recognition, and in particular relates to an unknown target recognition method based on a deep convolutional neural network. Background technique [0002] Since the middle of the last century, radar target recognition technology has gradually developed and matured. Judging the radar target to be recognized is mainly based on the radar cross-sectional area (RCS) or the one-dimensional range profile (HRRP). The high-resolution one-dimensional range profile is the vector sum of echoes from target scattering centers acquired by broadband radar, which not only provides the geometric shape and structural characteristics of the target, but also contains more relevant information required for target recognition. [0003] In recent years, deep learning theory has gradually matured, and convolutional neural networks have been widely used in the field of radar target recognition. Because of their tran...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S7/41
CPCG01S7/411G01S7/417
Inventor 周代英张同梦雪李粮余胡晓龙
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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