Experiencing and handling the diversity in data density and environmental locality in an indoor positioning service
Experiencing and handling the diversity in data density and environmental locality in an indoor positioning service
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DOI：
10.1145/2639108.2639118
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发表时间：
2014-09
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影响因子：
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通讯作者：
Liqun Li;G. Shen;Chunshui Zhao;T. Moscibroda;Jyh-Han Lin;Feng Zhao
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文献类型：
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作者：
Liqun Li;G. Shen;Chunshui Zhao;T. Moscibroda;Jyh-Han Lin;Feng Zhao
训练数据密度和环境局部性的多样性是室内定位系统的真实部署中固有的，并且对现有定位方法的性能具有重大影响。在本文中，通过微基准测试，我们发现，基于指纹的方法是更可取的情况下，密集的数据库是可用的，而基于模型的方法是在稀疏数据的情况下的选择方法。然而，应当指出，实际情况是复杂的。单个部署通常具有稀疏和密集采样区域。此外，内部布局影响无线电信号的传播，并表现出环境影响。一定数量的测量样本对于建筑物的一部分可能是足够的，但对于另一部分则完全不够。因此，为给定的大规模部署找到正确的室内定位算法是具有挑战性的，如果不是不可能的话;不存在一刀切的室内定位方法。实现的基本事实，即捕获实际的无线电地图的位置数据库的质量决定定位精度，在本文中，我们提出了Modellet，一种算法的方法，最佳地近似实际的无线电地图，通过统一的基于模型和基于指纹的方法。Modellet使用指纹云表示无线电地图，该指纹云结合了测量的真实的指纹和虚拟指纹，这些指纹是基于支持集的关键概念从具有本地支持的模型计算的。我们使用从办公楼以及13个大型部署场所（购物中心和机场）收集的数据对Modellet进行评估，这些场所位于中国，美国，和德国。将Modellet与两种具有代表性的基线方法RADAR和EZPerfect进行比较，表明Modellet有效地适应不同的数据密度和环境条件，大大优于现有方法。
Diversity in training data density and environment locality is intrinsic in the real-world deployment of indoor localization systems and has a major impact on the performance of existing localization approaches. In this paper, through micro-benchmarks, we find that fingerprint-based approaches are preferable in scenarios where a dense database is available; while model-based approaches are the method of choice in the case of sparse data. It should be noted, however, that practical situations are complex. A single deployment often features both sparse and dense sampled areas. Furthermore, the internal layout affects the propagation of radio signals and exhibits environmental impacts. A certain number of measurement samples may be sufficient for one part of the building, but entirely insufficient for another. Thus, finding the right indoor localization algorithm for a given large-scale deployment is challenging, if not impossible; there is no one-size-fits-all indoor localization approach. Realizing the fundamental fact that the quality of the location database capturing the actual radio map dictates localization accuracy, in this paper, we propose Modellet, an algorithmic approach that optimally approximates the actual radio map by unifying model-based and fingerprint-based approaches. Modellet represents the radio map using a fingerprint-cloud that incorporates both measured real fingerprints and virtual fingerprints, which are computed from models with a local support, based on the key concept of the supporting set. We evaluate Modellet with data collected from an office building as well as 13 large-scale deployment venues (shopping malls and airports), located across China, U.S., and Germany. Comparing Modellet with two representative baseline approaches, RADAR and EZPerfect, demonstrates that Modellet effectively adapts to different data densities and environmental conditions, substantially outperforming existing approaches.