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Random field-aided tracking of autonomous kinetically passive wireless agents
EURASIP Journal on Advances in Signal Processing ( IF 1.7 ) Pub Date : 2020-02-13 , DOI: 10.1186/s13634-019-0657-x
Stephan Schlupkothen , Tim Heidenblut , Gerd Ascheid

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.



中文翻译:

自主动力学被动无线代理的随机场辅助跟踪

电路的持续小型化为毫米级无线代理的各种新颖应用场景(例如,探索难以访问的流体环境)打开了大门。在这种情况下,设想使用试剂,例如用于管道检查或地下水分析。无论哪种情况,对微型传感器的需求都与推进能力不兼容。因此,这些药剂被定为是动力学被动的,因此受到环境中存在的流体动力学的影响。在这些情况下,由于未知的外力(例如,来自流体的外力)支配着药剂的运动,使得定位变得复杂。在这项工作中 提出了一个综合的框架,该框架旨在同时估计源于流体和介质在环境中的位置所产生的外力。更准确地说,提出了一种贝叶斯分层模型,该模型通过空间随机场对流体的相关特征进行建模,并将其作为控制输入合并到运动模型中。随机场模型有助于考虑代理轨迹之间的空间相关性,从而显着改善定位。另外,这与多重颗粒过滤相结合以解决以下事实:在这种地下流体环境中,仅基于距离和/或方位测量的定位是可行的。在这项工作提供的结果中,

更新日期:2020-04-21
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