  In modern radar systems, precise target localization using azimuth and
velocity estimation is paramount. Traditional unbiased estimation methods have
utilized gradient descent algorithms to reach the theoretical limits of the
Cramer Rao Bound (CRB) for the error of the parameter estimates. As an
extension, we demonstrate on a realistic simulated example scenario that our
earlier presented data-driven neural network model outperforms these
traditional methods, yielding improved accuracies in target azimuth and
velocity estimation. We emphasize, however, that this improvement does not
imply that the neural network outperforms the CRB itself. Rather, the enhanced
performance is attributed to the biased nature of the neural network approach.
Our findings underscore the potential of employing deep learning methods in
radar systems to achieve more accurate localization in cluttered and dynamic
environments.
