TY - GEN

T1 - Optimizing sensor combinations and processing parameters in dynamic sensor networks

AU - Garcia-Fernandez, Nicolas

AU - Schön, Steffen

N1 - Funding information: This work was supported by the German Research Foundation (DFG) as a part of the Research Training Group i.c.sens [GRK2159]. The computations were performed by the computer cluster, which is funded by the Leibniz Universität Hannover, the Lower Saxony Ministry of Science and Culture (MWK) and DFG.

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85075269517&partnerID=8YFLogxK

U2 - 10.33012/2019.16885

DO - 10.33012/2019.16885

M3 - Conference contribution

AN - SCOPUS:85075269517

T3 - Proceedings of the Satellite Division's International Technical Meeting

SP - 2048

EP - 2062

BT - Proceedings of the 32nd International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2019

T2 - 32nd International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2019

Y2 - 16 September 2019 through 20 September 2019

ER -