Duet
Duet
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DOI：
10.1145/3214287
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发表时间：
2018-07
影响因子：
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通讯作者：
Deepak Vasisht;Anubhav Jain;Chen-Yu Hsu;Zachary Kabelac;D. Katabi
中科院分区：
文献类型：
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作者：
Deepak Vasisht;Anubhav Jain;Chen-Yu Hsu;Zachary Kabelac;D. Katabi
尽管过去基于RF的室内定位工作取得了重要进展，但它通常会做出阻碍其在智能家居应用中采用的假设。大多数定位系统假设用户在家中随身携带手机，这一假设在过去的测量中被证明是非常不准确的。少数不需要用户随身携带设备的定位系统无法识别人的身份;但识别对于大多数智能家居应用至关重要。本文的重点是解决这些问题，使智能家居可以受益于室内定位的最新进展。我们介绍Duet，一个多模态系统，需要作为输入测量从基于设备和无设备的本地化。Duet引入了一个新的框架，将概率推理与一阶逻辑相结合，根据测量结果来推理用户最可能的位置和身份。我们实现Duet，并将其与使用最先进的基于WiFi的定位的基线进行比较。在两个智能环境中两周的监测结果显示，Duet在两地准确定位和识别用户的时间分别为94%和96%。相比之下，基线的准确率分别为17%和42%。
Although past work on RF-based indoor localization has delivered important advances, it typically makes assumptions that hinder its adoption in smart home applications. Most localization systems assume that users carry their phones on them at home, an assumption that has been proven highly inaccurate in past measurements. The few localization systems that do not require the user to carry a device on her, cannot tell the identity of the person; yet identification is essential to most smart home applications. This paper focuses on addressing these issues so that smart homes can benefit from recent advances in indoor localization. We introduce Duet, a multi-modal system that takes as input measurements from both device-based and device-free localization. Duet introduces a new framework that combines probabilistic inference with first order logic to reason about the users' most likely locations and identities in light of the measurements. We implement Duet and compare it with a baseline that uses state-of-art WiFi-based localization. The results of two weeks of monitoring in two smart environments show that Duet accurately localizes and identifies the users for 94% and 96% of the time in the two places. In contrast, the baseline is accurate 17% and 42% respectively.