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Omnidirectional Coverage for Device-Free Passive Human Detection

机译：全向覆盖，无需设备进行被动人体检测

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摘要

Device-free Passive (DfP) human detection acts as a key enabler for emerging location-based services such as smart space, human-computer interaction, and asset security. A primary concern in devising scenario-tailored detecting systems is coverage of their monitoring units. While disk-like coverage facilitates topology control, simplifies deployment analysis, and is crucial for proximity-based applications, conventional monitoring units demonstrate directional coverage due to the underlying transmitter-receiver link architecture. To achieve omnidirectional coverage under such link-centric architecture, we propose the concept of omnidirectional passive human detection. The rationale is to exploit the rich multipath effect to blur the directional coverage. We harness PHY layer features to robustly capture the fine-grained multipath characteristics and virtually tune the shape of the coverage of the monitoring unit, which is previously prohibited with mere MAC layer RSSI. We design a fingerprinting scheme and a threshold-based scheme with off-the-shelf WiFi infrastructure and evaluate both schemes in typical clustered indoor scenarios. Experimental results demonstrate an average false positive of 8 percent and an average false negative of 7 percent for fingerprinting in detecting human presence in 4 directions. And both average false positive and false negative remain around 10 percent even with threshold-based methods.
机译：无设备被动（DfP）人工检测是新兴的基于位置的服务（例如智能空间，人机交互和资产安全）的关键推动力。在设计针对情境的检测系统时，主要关注的是其监视单元的覆盖范围。尽管类似磁盘的覆盖范围有助于拓扑控制，简化部署分析，并且对于基于邻近的应用至关重要，但由于底层的收发器链接体系结构，传统的监视单元仍显示出定向覆盖范围。为了在这种以链接为中心的体系结构下实现全向覆盖，我们提出了全向被动人体检测的概念。基本原理是利用丰富的多径效应来模糊定向覆盖范围。我们利用PHY层功能来稳健地捕获细粒度的多径特性，并虚拟地调整监视单元覆盖范围的形状，而以前仅MAC层RSSI禁止这样做。我们使用现成的WiFi基础架构设计了指纹方案和基于阈值的方案，并在典型的群集室内场景中评估了这两种方案。实验结果表明，在检测4个方向上的人类存在时，指纹识别的平均误报率为8％，平均误报率为7％。即使使用基于阈值的方法，平均误报率和误报率均保持在10％左右。

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