18 citations · 24 across the 12 of their papers we have counts for
7 papers · 1 filter
Bayesian Jammer Localization with a Hybrid CNN and Path-Loss Mixture of Experts
Mariona Jaramillo-Civill, Luis González-Gudiño, Tales Imbiriba +1
Global Navigation Satellite System (GNSS) signals are vulnerable to jamming, particularly in urban areas where multipath and shadowing distort received power. Previous data-driven…
Active Jammer Localization via Acquisition-Aware Path Planning
Luis González-Gudiño, Mariona Jaramillo-Civill, Pau Closas +1
We propose an active jammer localization framework that combines Bayesian optimization with acquisition-aware path planning. Unlike passive crowdsourced methods, our approach adapt…
DPMM-CFL: Clustered Federated Learning via Dirichlet Process Mixture Model Nonparametric Clustering
Mariona Jaramillo-Civill, Peng Wu, Pau Closas
Clustered Federated Learning (CFL) improves performance under non-IID client heterogeneity by clustering clients and training one model per cluster, thereby balancing between a glo…
Recursive Deep Inverse Reinforcement Learning
Paul Ghanem, Owen Howell, Michael Potter +4
Inferring an adversary's goals from exhibited behavior is crucial for counterplanning and non-cooperative multi-agent systems in domains like cybersecurity, military, and strategy…
A Bayesian Framework for Clustered Federated Learning
Peng Wu, Tales Imbiriba, Pau Closas
One of the main challenges of federated learning (FL) is handling non-independent and identically distributed (non-IID) client data, which may occur in practice due to unbalanced d…
AI-Aided Kalman Filters
Nir Shlezinger, Guy Revach, Anubhab Ghosh +7
The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing. These methods are used for state estimation of dynamic systems by relying on…
On the Impact of Sampling on Deep Sequential State Estimation
Helena Calatrava, Ricardo Augusto Borsoi, Tales Imbiriba +1
State inference and parameter learning in sequential models can be successfully performed with approximation techniques that maximize the evidence lower bound to the marginal log-l…