  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.
