Omnidirectional coverage for device-free passive human detection
IEEE Transactions on Parallel and Distributed Systems, ISSN: 1045-9219, Vol: 25, Issue: 7, Page: 1819-1829
2014
- 77Citations
- 8Usage
- 75Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations77
- Citation Indexes76
- 76
- CrossRef60
- Patent Family Citations1
- 1
- Usage8
- Abstract Views8
- Captures75
- Readers75
- 75
Article Description
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. © 2013 IEEE.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84903132427&origin=inward; http://dx.doi.org/10.1109/tpds.2013.274; http://ieeexplore.ieee.org/document/6648318/; http://xplorestaging.ieee.org/ielx7/71/6828815/06648318.pdf?arnumber=6648318; https://ink.library.smu.edu.sg/sis_research/4606; https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5609&context=sis_research
Institute of Electrical and Electronics Engineers (IEEE)
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