Data-Driven Target Localization: Benchmarking Gradient Descent Using the Cram\'er-Rao Bound

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 …

algorithms benchmarking cs.lg data data-driven eess.sp error extension gradient localization modern radar systems unbiased