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Daniel Knobloch

dblp:202/5538 · DBLP profile ↗
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2ranked-venue papers
1as first author
1since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Wireless sensing and localization · 100%
Artificial intelligence
1 paper
Generative modeling · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Wireless sensing and localization › RF-based localization
UWB localization		 0.612022
Domain Reconstruction for UWB Car Key Localization Using Generative Adversarial Networks · AAAI 2022
Machine learning › Generative modeling
generative adversarial network		 0.212022
Domain Reconstruction for UWB Car Key Localization Using Generative Adversarial Networks · AAAI 2022

Methods — techniques the papers use, named apart from their topics

generative adversarial network · 1.1
YearPublicationVenuePosition
2022 Domain Reconstruction for UWB Car Key Localization Using Generative Adversarial Networks
Aleksei Kuvshinov, Daniel Knobloch, Daniel Külzer, Elen Vardanyan, Stephan Günnemann
 AAAI2
2017 Practical challenges of particle filter based UWB localization in vehicular environments
abstract
In this paper, the challenges of Ultra Wide Band localization for key fob applications in vehicular environments are discussed. Trilateration based Kalman and particle filter approaches are discussed regarding their applicability to this task. A particle filter method is proposed which combines the benefit of enabling non-Gaussian observation error distribution and direct mapping of a particle cloud to a vehicle zone. The presented measurements show a strong sensitivity on the knowledge of line-of-sight or non-line-of-sight conditions. It is concluded that correct estimation of the channel condition is the key factor in improving the performance of the proposed method.
Daniel Knobloch
 IPIN1