Search Results for author:
Found 8 papers, 0 papers with code
Fisher Information Approach for Masking the Sensing Plan: Applications in Multifunction Radars
How to design a Markov Decision Process (MDP) based radar controller that makes small sacrifices in performance to mask its sensing plan from an adversary?
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.
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.
Radar Clutter Covariance Estimation: A Nonlinear Spectral Shrinkage Approach
We demonstrate that the computation time for the estimation by the proposed algorithm is less than the RCML-EL algorithm with identical Signal to Clutter plus Noise (SCNR) performance.
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.
High Fidelity RF Clutter Modeling and Simulation
In this paper, we present a tutorial overview of state-of-the-art radio frequency (RF) clutter modeling and simulation (M&S) techniques.
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.
Adversarial Radar Inference: Inverse Tracking, Identifying Cognition and Designing Smart Interference
The levels of abstraction range from smart interference design based on Wiener filters (at the pulse/waveform level), inverse Kalman filters at the tracking level and revealed preferences for identifying utility maximization at the systems level.
