Deep Learning-based Estimation for Multitarget Radar Detection
This work addresses target detection and recognition in wireless environments, offering incremental improvements for radar systems.
The paper tackles the problem of estimating range and velocity for multiple moving targets in radar detection by proposing a convolutional neural network (CNN) method that directly processes range-Doppler maps, achieving improved accuracy and reduced prediction time compared to existing methods, with gains of up to 33 dB in PSNR at SNR = 30 dB.
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.