74 citations · 84 across the 7 of their papers we have counts for
8 papers
Enhancing UAV Search under Occlusion using Next Best View Planning
Sigrid Helene Strand, Thomas Wiedemann, Bram Burczek +1
Search and rescue missions are often critical following sudden natural disasters or in high-risk environmental situations. The most challenging search and rescue missions involve d…
Semantic Joint Source Channel Coding for Distributed Subsurface Imaging in Multi-Agent Systems
Maximilian H. V. Tillmann, Ban-Sok Shin, Dmitriy Shutin +1
Multi-agent systems (MASs) are a promising solution for autonomous exploration tasks in hazardous or remote environments. In such settings, communication among agents is essential…
Mobile-to-Mobile Uncorrelated Scatter Channels
Michael Walter, Martin Schmidhammer, Miguel A. Bellido-Manganell +2
In this paper, we present a complete analytic probability based description of mobile-to-mobile uncorrelated scatter channels. The correlation based description introduced by Bello…
Gas Source Localization Using physics Guided Neural Networks
Victor Scott Prieto Ruiz, Patrick Hinsen, Thomas Wiedemann +2
This work discusses a novel method for estimating the location of a gas source based on spatially distributed concentration measurements taken, e.g., by a mobile robot or flying pl…
Deep Reinforcement Multi-agent Learning framework for Information Gathering with Local Gaussian Processes for Water Monitoring
Samuel Yanes Luis, Dmitriy Shutin, Juan Marchal Gómez +2
The conservation of hydrological resources involves continuously monitoring their contamination. A multi-agent system composed of autonomous surface vehicles is proposed in this pa…
Joint Detection and Super-Resolution Estimation of Multipath Signal Parameters Using Incremental Automatic Relevance Determination
Dmitriy Shutin, Nicolas Schneckenburger
The presented work investigates a sparse Bayesian incremental automatic relevance determination (IARD) algorithm in the context of multipath parameter estimation in a super-resolut…
Application of Bayesian Hierarchical Prior Modeling to Sparse Channel Estimation
Niels Lovmand Pedersen, Carles Navarro Manchón, Dmitriy Shutin +1
Existing methods for sparse channel estimation typically provide an estimate computed as the solution maximizing an objective function defined as the sum of the log-likelihood func…
Sparse Estimation using Bayesian Hierarchical Prior Modeling for Real and Complex Linear Models
Niels Lovmand Pedersen, Carles Navarro Manchón, Mihai-Alin Badiu +2
In sparse Bayesian learning (SBL), Gaussian scale mixtures (GSMs) have been used to model sparsity-inducing priors that realize a class of concave penalty functions for the regress…