Data-Driven Target Localization: Benchmarking Gradient Descent Using the Cramer-Rao Bound
This work addresses the problem of precise target localization for radar systems, offering incremental improvements through a biased neural network approach.
The paper tackled target localization in radar systems by comparing a data-driven neural network model to traditional gradient descent methods, showing that the neural network achieved improved accuracies in azimuth and velocity estimation on a simulated scenario, though it did not outperform the theoretical Cramer-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 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.