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Found 7 papers, 0 papers with code
On Bounds for Greedy Schemes in String Optimization based on Greedy Curvatures
We prove that our bound is superior to the greedy curvature bound of Conforti and Cornu\'ejols.
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.
Minimax Concave Penalty Regularized Adaptive System Identification
We develop a recursive least square (RLS) type algorithm with a minimax concave penalty (MCP) for adaptive identification of a sparse tap-weight vector that represents a communication channel.
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.
Bayesian Learning of Occupancy Grids
Occupancy grids encode for hot spots on a map that is represented by a two dimensional grid of disjoint cells.
Hypothesis Testing in Feedforward Networks with Broadcast Failures
In the case where the flipping probabilities converge to 1/2, we derive a necessary condition on the convergence rate of the flipping probabilities such that the decisions still converge to the underlying truth.
