Mobile Node Localization Focusing on Stop-and-Go Behavior of Indoor Pedestrians
Mobile Node Localization Focusing on Stop-and-Go Behavior of Indoor Pedestrians
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DOI：
10.1109/tmc.2013.139
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发表时间：
2014-07
影响因子：
7.9
通讯作者：
Takamasa Higuchi;Sae Fujii;Hirozumi Yamaguchi;T. Higashino
中科院分区：
文献类型：
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作者：
Takamasa Higuchi;Sae Fujii;Hirozumi Yamaguchi;T. Higashino
尽管最近在用于移动的设备的定位技术方面取得了进展，但是向室内的人提供实时位置信息仍然是一个很大的挑战;通常在定位精度和基础设施成本之间存在权衡（例如，密集的锚部署）。一种可能的解决方案是采用协作方法，该方法利用周围移动的节点的估计位置来补充少量的锚点。然而，由于由于节点移动性导致的临时大的位置误差容易传播到相邻节点，因此它经常导致差的估计精度。本文提出了一种新的合作定位算法，解决了这个问题，专注于“走走停停的行为”的室内行人。该算法的核心思想是基于对等距离测量，协同发现每个节点的运动状态（运动或静止），并仅使用静止节点作为参考点进行定位，以避免潜在的精度恶化。此外，节点在静止状态下可以减少定位频率，以节省电池电量，保持跟踪质量。通过大量的模拟，我们已经证明了我们的方法在准确性和能源效率方面的性能。使用基于测量的传感器模型和真实的移动轨迹也证实了在真实的应用场景中的有效性。
Despite recent advances in localization technology for mobile devices, to provide real-time position information to people indoors is still a big challenge; usually there is a trade-off between localization accuracy and infrastructural costs (e.g., dense anchor deployment). A possible solution would be employing cooperative approaches which utilize estimated positions of surrounding mobile nodes to complement a small number of anchors. However, it often results in poor estimation accuracy since a temporary large position error due to node mobility easily propagates to neighbor nodes. This paper presents a novel cooperative localization algorithm that addresses this problem by focusing on “stop-and-go behavior” of indoor pedestrians. The key idea is to collaboratively find movement state (moving or static) of each node based on peer-to-peer distance measurement which is inherently necessary for cooperative localization, and use only static nodes as reference points for localization to avoid potential accuracy deterioration. Also, nodes in static state can reduce localization frequency to conserve battery power, keeping the tracking quality. Through extensive simulations, we have demonstrated the performance of our method in terms of accuracy and energy efficiency. The effectiveness in a real application scenario has been also confirmed using a measurement-based sensor model and real mobility traces.