SCPL: Indoor Device-Free Multi-Subject Counting and Localization Using Radio Signal Strength Chenren Xu†, Bernhard Firner†, Robert S. Moore , Yanyong Zhang† ∗ Wade Trappe†, Richard Howard†, Feixiong Zhang†, Ning An§ †WINLAB, Rutgers University, North Brunswick, NJ, USA ∗Computer Science Dept, Rutgers University, Piscataway, NJ, USA §Gerontechnology Lab, Hefei University of Technology, Hefei, Anhui, China IPSN 2013
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SCPL: Indoor Device-Free Multi-Subject Counting and Localization Using Radio Signal Strength Chenren Xu†, Bernhard Firner†, Robert S. Moore ∗, Yanyong.
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SCPL: Indoor Device-Free Multi-Subject Counting andLocalization Using Radio Signal Strength
Chenren Xu†, Bernhard Firner†, Robert S. Moore , Yanyong Zhang†∗Wade Trappe†, Richard Howard†, Feixiong Zhang†, Ning An§
†WINLAB, Rutgers University, North Brunswick, NJ, USA∗Computer Science Dept, Rutgers University, Piscataway, NJ, USA
§Gerontechnology Lab, Hefei University of Technology, Hefei, Anhui, China
IPSN 2013
About This Paper
• Indoor localization technique– RF-based device-free passive localization– Fingerprinting based approach– Count and track multiple subjects
• The first work to simultaneous counting and localizing– Up to 4 objects– Only using RF-based technique
• Relying on data collected by single subjects• Trajectory constraints to improve tracking
accuracy• Recognize the nonlinear fading effects– Cause by multiple subjects
Problem Formulation
• Partition into K cells• Training phase– Measure ambient RSS value for L links– A single subject appear in single cell
(randomly walk within cell)• Take N measurement for L links• Subtract ambient RSS• Dataset D: K * N * L matrix
– Subject’s present in Cell i: State Si
• DS1, DS1, DS1 ,……, DSk
Problem Formulation
• Testing phase– Measure ambient RSS for L links– A subject appears in random cell• Measure RSS for all L links• Subtract ambient• Form an RSS vector O
• Computation complexity– 0.87s and 0.88s for 4 objects– More that 1s for 5 objects or above
• Long-term test– Suffer from environmental change– Fingerprint aging
Conclusion
• Device free localization system• Track multiple subjects• Average 86% counting accuracy ??• Average 1.3m localization accuracy ??• Test in two different environments– How many iteration?