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Found 5 papers, 0 papers with code
Data-Driven Target Localization: Benchmarking Gradient Descent Using the Cramer-Rao Bound
In modern radar systems, precise target localization using azimuth and velocity estimation is paramount.
Random Linear Projections Loss for Hyperplane-Based Optimization in Neural Networks
Advancing loss function design is pivotal for optimizing neural network training and performance.
Subspace Perturbation Analysis for Data-Driven Radar Target Localization
Via the use of space-time adaptive processing (STAP) techniques and convolutional neural networks, these data-driven approaches to target localization have helped benchmark the performance of neural networks for matched scenarios.
Data-Driven Target Localization Using Adaptive Radar Processing and Convolutional Neural Networks
Leveraging the advanced functionalities of modern radio frequency (RF) modeling and simulation tools, specifically designed for adaptive radar processing applications, this paper presents a data-driven approach to improve accuracy in radar target localization post adaptive radar detection.
Toward Data-Driven STAP Radar
In this regard, we will generate a large, representative adaptive radar signal processing database for training and testing, analogous in spirit to the COCO dataset for natural images.
