Optimizing sensor combinations and processing parameters in dynamic sensor networks

verfasst von
Nicolas Garcia-Fernandez, Steffen Schön

The algorithms used to provide robust Position, Navigation and Timing (PNT) information for autonomous navigation purposes normally rely on accurate, precise, reliable and continuous information, captured with different sensors mounted on the vehicles. In addition, the availability of these sensors and the growth and development of the wireless communication systems enable the distribution of the information between both dynamic and fixed agents of the scene (Collaborative/Cooperative Positioning, CP). In collaborative scenarios, the characteristics of the used sensors (precision, geometry, limitations, etc.) together with the heterogeneous environments in which the vehicles navigate reveal that a single fixed sensor configuration might not be always optimum. This paper discusses the results from an in-house developed simulation tool that enables and assists the optimum selection of sensors and processing parameters for collaborative navigation in dynamic sensor networks by means of Monte Carlo techniques. Given that the sensor characteristics and the chosen processing parameters in the simulation are often associated with the sensor costs, the reader will learn from the outcome of the study the best performing sensor combinations that drive the cost of the sensor combination down, but still achieve the desired performance.

Institut für Erdmessung
Aufsatz in Konferenzband
Anzahl der Seiten
ASJC Scopus Sachgebiete
Kommunikation, Angewandte Informatik, Information systems, Software, Elektrotechnik und Elektronik, Computernetzwerke und -kommunikation
Elektronische Version(en)
https://doi.org/10.33012/2019.16885 (Zugang: Geschlossen)

Details im Forschungsportal „Research@Leibniz University“