Current browse context:
eess.SP
Change to browse by:
References & Citations
Electrical Engineering and Systems Science > Signal Processing
Title: Data-Driven Target Localization: Benchmarking Gradient Descent Using the Cramer-Rao Bound
(Submitted on 20 Jan 2024 (v1), last revised 22 Apr 2024 (this version, v3))
Abstract: 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.
Submission history
From: Shyam Venkatasubramanian [view email][v1] Sat, 20 Jan 2024 09:26:08 GMT (4157kb,D)
[v2] Fri, 9 Feb 2024 09:11:43 GMT (4173kb,D)
[v3] Mon, 22 Apr 2024 22:02:34 GMT (4173kb,D)
Link back to: arXiv, form interface, contact.