High Fidelity RF Clutter Modeling and Simulation
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Abstract (arXiv)
In this paper, we present a tutorial overview of state-of-the-art radio frequency (RF) clutter modeling and simulation (M&S) techniques. Traditional statistical approximation based methods will be reviewed followed by more accurate physics-based stochastic transfer function clutter models that facilitate site-specific simulations anywhere on earth. The various factors that go into the computation of these transfer functions will be presented, followed by several examples across multiple RF applications. Finally, we introduce a radar challenge dataset generated using these tools that can enable testing and benchmarking of all cognitive radar algorithms and techniques.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Feb 2022.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 1.
3 papers.
- 2022Data-Driven Target Localization Using Adaptive Radar Processing and Convolutional Neural Networkscited by 3
- 2023Subspace Perturbation Analysis for Data-Driven Radar Target Localizationcited by 1
- 2024Data-Driven Target Localization: Benchmarking Gradient Descent Using the Cramer-Rao Boundcited by 0
- 2024Data-Driven Target Localization: Benchmarking Gradient Descent Using the Cramer-Rao Bound20 Jan 2024
- 2023Subspace Perturbation Analysis for Data-Driven Radar Target Localization14 Mar 2023
- 2022Data-Driven Target Localization Using Adaptive Radar Processing and Convolutional Neural Networks7 Sep 2022