A target recognition method for sar raw data based on resnet18

A target recognition and original data technology, applied in character and pattern recognition, instruments, biological neural network models, etc., can solve the problems of old CNN architecture, small scale, and affecting the accuracy of target recognition, and achieve low complexity and network training Easier to optimize and identify better classification performance

Active Publication Date: 2022-08-05
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

The first framework works directly in the phase history domain, while the other framework includes three steps of image reconstruction, dephasing the image and returning it to the phase history domain, but the input part of the method utilizes an approximation of the phase history instead of The actual value often affects the accuracy of target recognition
In addition, the CNN architecture used is relatively old and small

Method used

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  • A target recognition method for sar raw data based on resnet18
  • A target recognition method for sar raw data based on resnet18
  • A target recognition method for sar raw data based on resnet18

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Embodiment

[0065] In order to verify the effectiveness of SAR raw data target recognition based on ResNet18, experiments are carried out on the simulated dataset and MSTAR dataset respectively. In addition, in order to illustrate the advantages of SAR raw data target recognition based on ResNet18, the target recognition results obtained by this method are compared with the SAR image target recognition results based on ResNet18 and the SAR raw data target recognition results based on other CNNs.

[0066] Image 6 In order to simulate the network training process of the data set, the network training time is 4 minutes and 26 seconds, and the recognition accuracy is 99.6%. It can be seen from the results that under the condition of simple and small samples, the network using the SAR receiving signal as the input The model recognition rate reaches more than 99%, the network training efficiency is high and the target classification can be well achieved.

[0067] Figure 7 It is the network ...

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Abstract

The invention discloses a ResNet18-based SAR raw data target recognition method, relates to the technical field of radar signal processing, and proposes an automatic target recognition (Automatic Target Recognition, Synthetic Aperture Radar, SAR) raw data as input. ATR) method. Existing ATR methods perform the task after the image is formed. However, the imaging process of target information may lose the target abstract feature information hidden in the original data, thus limiting the accuracy of recognition. Therefore, based on the deep residual network ResNet18, the present invention sends the SAR raw data into a convolutional neural network framework that does not require image reconstruction to perform target recognition and classification, thereby significantly improving the recognition efficiency and obtaining better classification results. .

Description

technical field [0001] The invention relates to the technical field of radar signal processing, in particular to a ResNet18-based SAR raw data target identification method. Background technique [0002] With the rapid development of modern information technology and its wide application in the military field, target recognition technology has a wide range of applications in military fields such as early warning detection, precision guidance, battlefield command and identification. Automatic Target Recognition (ATR) of Synthetic Aperture Radar (SAR) refers to the task of finding and identifying targets, and its purpose is to identify target features by acquiring attribute information in images. However, the existing SAR target recognition technology generally has the shortcomings of low intelligence and poor real-time performance. Therefore, research on more intelligent and efficient target recognition technology has become an important requirement. [0003] In recent years...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V20/10G06V10/774G06V10/82G06K9/62G06N3/04
CPCG06N3/045G06F18/214
Inventor 汪玲阮西玥郭军胡长雨
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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