Multi-step sensor selection with position uncertainty constraints

Fröhle, Markus; Zaidi, Ali A.; Ström, Erik G.; Wymeersch, Henk

doi:10.1109/glocomw.2014.7063636

Cited by 3 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This allows us to run the Viterbi algorithm and efficiently compute the cost of any combination of measurement system selections. Additional details can be found in [9].…”

Section: Optimisation Formulationmentioning

confidence: 99%

Formation Control of Multi-Agent Systems with Location Uncertainty

Fröhle

Charalambous²,

Wymeersch

et al. 2017

Multi-Technology Positioning

Self Cite

View full text Add to dashboard Cite

Increasingly sophisticated algorithms implemented on autonomous agents/robots with massive computational capabilities have enabled solving complex tasks in uncertain environments, such as mapping of disaster areas and search-and-rescue operations. For agents to explore and interact with the environment, it is important that they have a coherent view of this environment and their positions within it (see, for example, [19,25] and references therein). Such situational awareness is typically achieved through simultaneous localisation and mapping (SLAM) [32] and localisation using heterogeneous sensor fusion [36]. While research on situational awareness has traditionally focused on improving the localisation accuracy, the focus has now shifted to localisation methods that have knowledge of computational complexity limitations of the agents, energy and communication constraints as well as the agent's higher-level task [8,10]; for instance, a higher-level task may be the navigation of the robot in the environment from its current position to its final position. This chapter considers the problem of how position uncertainty in M. Fröhle ( ) • H. Wymeersch

show abstract

“…This allows us to run the Viterbi algorithm and efficiently compute the cost of any combination of measurement system selections. Additional details can be found in [9].…”

Section: Optimisation Formulationmentioning

confidence: 99%

Formation Control of Multi-Agent Systems with Location Uncertainty

Fröhle

Charalambous²,

Wymeersch

et al. 2017

Multi-Technology Positioning

Self Cite

View full text Add to dashboard Cite

show abstract

“…In robotics, predictive optimal control is used to actively plan a robot's path for achieving certain goals under constraints [25], [26] or for efficiently building better maps of environments in simultaneous localization and mapping (SLAM) [27]- [30]. In [31] decentralized information-theoretic control of autonomous systems has been analyzed with multiple sensors.…”

Section: A Motivation and State Of The Artmentioning

confidence: 99%

Cognitive Indoor Positioning and Tracking using Multipath Channel Information

Leitinger¹,

Meißner²,

Witrisal³

2016

Preprint

View full text Add to dashboard Cite

This paper presents a robust and accurate positioning system that adapts its behavior to the surrounding environment like the visual brain, mimicking its capability of filtering out clutter and focusing attention on activity and relevant information. Especially in indoor environments, which are characterized by harsh multipath propagation, it is still elusive to achieve the needed level of accuracy robustly under the constraint of reasonable infrastructural needs. In such environments it is essential to separate relevant from irrelevant information and attain an appropriate uncertainty model for measurements that are used for positioning.

show abstract