Deep Learning-based Estimation for Multitarget Radar Detection
- URL: http://arxiv.org/abs/2305.05621v1
- Date: Fri, 5 May 2023 16:22:17 GMT
- Title: Deep Learning-based Estimation for Multitarget Radar Detection
- Authors: Mamady Delamou, Ahmad Bazzi, Marwa Chafii and El Mehdi Amhoud
- Abstract summary: We propose a new method based on a convolutional neural network (CNN) to estimate the range and velocity of moving targets directly from the range-Doppler map of the detected signals.
Compared to the 2D-periodogram, 2DResFreq and VGG-19, we gain 33 dB, 21 dB and 10 dB, respectively, in terms of PSNR when SNR = 30 dB.
- Score: 11.623005206620496
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Target detection and recognition is a very challenging task in a wireless
environment where a multitude of objects are located, whether to effectively
determine their positions or to identify them and predict their moves. In this
work, we propose a new method based on a convolutional neural network (CNN) to
estimate the range and velocity of moving targets directly from the
range-Doppler map of the detected signals. We compare the obtained results to
the two dimensional (2D) periodogram, and to the similar state of the art
methods, 2DResFreq and VGG-19 network and show that the estimation process
performed with our model provides better estimation accuracy of range and
velocity index in different signal to noise ratio (SNR) regimes along with a
reduced prediction time. Afterwards, we assess the performance of our proposed
algorithm using the peak signal to noise ratio (PSNR) which is a relevant
metric to analyse the quality of an output image obtained from compression or
noise reduction. Compared to the 2D-periodogram, 2DResFreq and VGG-19, we gain
33 dB, 21 dB and 10 dB, respectively, in terms of PSNR when SNR = 30 dB.
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