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An Unknown Object Recognition Method Based on Deep Convolutional Neural Networks

A technology of deep convolution and neural network, applied in the field of unknown target recognition based on deep convolutional neural network, can solve the problem of not being able to recognize unknown targets, and achieve the effect of not being able to recognize unknown targets

Active Publication Date: 2022-08-02
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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  • An Unknown Object Recognition Method Based on Deep Convolutional Neural Networks
  • An Unknown Object Recognition Method Based on Deep Convolutional Neural Networks
  • An Unknown Object Recognition Method Based on Deep Convolutional Neural Networks

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Embodiment Construction

[0024] The effectiveness of the present invention is proved below in conjunction with simulation examples.

[0025] The simulation one-dimensional range images of five different types of military aircraft, AH64, AN26, F15, B1B, and B52, obtained by the special electromagnetic simulation characteristic scene are used for experiments. 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 at an elevation angle of 3° and an azimuth angle of 0° to 180° every 0.1°. Each type of aircraft collects 1801 one-dimensional range images, each one-dimensional range image. Each contains 320 distance units, that is, the input data of each type of aircraft is a one-dimensional distance image matrix of 1801 × 320.

[0026] In the process of training and updating the parameter W, randomly initialize the weight W=[w 1 ,w 2 ,w 3 ] and bias...

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Abstract

The invention belongs to the technical field of unknown target recognition, in particular to an unknown target recognition method based on a deep convolutional neural network. The invention firstly preprocesses the one-dimensional range image data (HRRP) obtained by the broadband radar to reduce the amplitude sensitivity of the one-dimensional range image; secondly, the deep convolutional neural network is used to extract features; finally, the difference probability method is used to process the known The recognition probability of the target data is obtained, the discrimination threshold is obtained, and the output vector of the neuron network is discriminated to identify the unknown target. Due to the introduction of the discriminant threshold obtained by the difference probability method, this method effectively describes the statistical distribution area boundary between the known target and the unknown target data set, and solves the problem that the conventional convolutional neural network cannot identify the unknown target.

Description

technical field [0001] The invention belongs to the technical field of unknown target recognition, in particular 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 matured, and the target to be recognized by radar is judged mainly based on radar target cross-sectional area (RCS) or one-dimensional range profile (HRRP). The high-resolution one-dimensional range is like the vector sum of the target scattering center echo obtained by the 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, the theory of deep learning has gradually matured, and convolutional neural networks have been widely used in the field of radar target recognition. Because of its translation insensitivi...

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Application Information

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Patent Type & Authority Patents(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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