Optimizing Sensor Combinations and Processing Parameters in Dynamic Sensor Networks

Nicolas Garcia-Fernandez and Steffen Schön

Abstract: 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.
Published in: Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019)
September 16 - 20, 2019
Hyatt Regency Miami
Miami, Florida
Pages: 2048 - 2062
Cite this article: Garcia-Fernandez, Nicolas, Schön, Steffen, "Optimizing Sensor Combinations and Processing Parameters in Dynamic Sensor Networks," Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019), Miami, Florida, September 2019, pp. 2048-2062.
https://doi.org/10.33012/2019.16885
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