Abstract
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.
Highlights
Technological advances played a pivotal role in leveraging the use of miniature wireless agents for novel application cases
9.2 Input-aided particle filtering vs. random field-aided tracking In this subsection, the input-aided particle filter (IPF) algorithm, which has been found to yield the better performance in the results presented above, is compared to the RFaT algorithm
The estimation of spatial properties modeled by means of an RF is combined with distance- and/or bearing-measurement-based localization
Summary
Technological advances played a pivotal role in leveraging the use of miniature wireless agents for novel application cases. The ACI ui,k takes the role of the control input (CI) in the case of kinetically active agents, with the only difference that the former is unknown and, needs to be estimated This modeling is complicated by the fact that the agents are operating in spatially confined areas where boundary effects are relevant, considering these effects are pivotal for improving the localization accuracy through the abovementioned RF model. Inference indirectly via localization, using distance and/or bearing measurements ††Distributed localization is considered which is considered sub-optimal in the scenario considered in this work due to the availability of all data in the FC ‡AWGN corrupted positions estimates are assumed §Affine dynamics are considered ∗Position-independent RVs whose parameters are fixed a priori are used instead of spatial field.
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