Random field-aided tracking of autonomous kinetically passive wireless agents

EURASIP journal on advances in signal processing

article

Schlupkothen, Stephan¹  Heidenblut, Tim¹  Ascheid, Gerd¹ 

[1] Chair for Integrated Signal Processing Systems, RWTH Aachen University

关键词: Wireless sensor networks;    Random field;    Tracking;    Multiple particle filtering;   

DOI  :  10.1186/s13634-019-0657-x

来源: SpringerOpen

【 摘 要 】

Continuous miniaturization of circuitry has open the door for various novel application scenarios of millimeter-sized wireless agents such as for the exploration of difficult-to-access fluid environments. In this context, agents are envisioned to be employed, e.g., for pipeline inspection or groundwater analysis. In either case, the demand for miniature sensors is incompatible with propulsion capabilities. Consequently, the agents are condemned to be kinetically passive and are, thus, subject to the fluid dynamics present in the environment. In these situations, the localization is complicated by the fact that unknown external forces (e.g., from the fluid) govern the motion of the agents. In this work, a comprehensive framework is presented that targets the simultaneous estimation of the external forces stemming from the fluid and the agents’ positions which are traversing the environment. More precisely, a Bayesian hierarchical model is proposed that models’ relevant characteristics of the fluid via a spatial random field and incorporates this as control input into the motion model. The random field model facilitates the consideration of spatial correlation among the agents’ trajectories and, thereby, improves the localization significantly. Additionally, this is combined with multiple particle filtering to account for the fact that within such underground fluid environments, only a localization based on distance and/or bearing measurements is feasible. In the results provided in this work, which are based on realistic computational fluid dynamics simulations, it is shown that—via the proposed spatial model—significant improvements in terms of localization accuracy can be achieved.

【 授权许可】

CC BY