Zhurnal Radioelektroniki - Journal of Radio Electronics. eISSN 1684-1719. 2022. ¹6
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DOI: https://doi.org/10.30898/1684-1719.2022.6.8

UDC: 621.396.96

 

MILITARY OBJECTS DETECTION

USING A CONVOLUTIONAL NEURAL NETWORK

ON RADAR IMAGES
FORMED IN JAMMING CONDITIONS

 

I.F. Kupryashkin

 

Military Educational and Scientific Center of the Air Force

«N.E. Zhukovsky and Y.A. Gagarin Air Force Academy» (Voronezh)
 394064, Russia, Voronezh, Starykh Bolshevikov St., 64

 

The paper was received May 17, 2022.

 

Abstract. The objects detector on radar images description is given, which implements the local inhomogeneities detection using the CFAR algorithm with their subsequent two-alternative classification («object» or «background») by a deep convolutional neural networkþ. Using the MSTAR dataset, it was shown that in the case of a homogeneous background, the detection results can be considered satisfactory if SNR on image is more than 5 dB, and completely disrupted if SNR less then minus 5 dB.

Key words: deep convolutional neural network, transfer learning, radar image, classification accuracy, object detection.

Corresponding author: Kupryashkin Ivan Fedorovich, ifk78@mail.ru

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For citation:

Kupryashkin I.F. Military objects detection using a convolutional neural network on radar images formed in jamming conditions. Zhurnal radioelectroniki [Journal of radio electronics]. 2022. ¹6. https://doi.org/10.30898/1684-1719.2022.6.8