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
About This Paper
• Indoor localization technique
– RF-based device-free passive localization
– Fingerprinting based approach
– Count and track multiple subjects
• Result
– Counting accuracy: 86%
– Localization accuracy: 1.3m
Contributions
• 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
• Compare D and O
– Classification algorithm
Outline
•
•
•
•
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Counting multiple subjects
Localizing multiple subjects
Experimental setup and result
Limitation
Conclusion
Impact of Multiple Subject
• Hypothesis: more subjects
– Not only affect more links
– But also higher level of RSS change
• Infer the number of subjects by RSS change
– Total energy change:
– Absolute RSS mean difference
• Distance between subjects
– Distance > 4m  faraway
– Else  closeby
Counting Subjects
• Successive cancellation
– In each round, estimate the strongest subject’s
cell number
– Subtract it share of RSS change
• If (Impact from multiple subjects is linear)
– Subtract the mean vector
• But the impact is Nonlinear
– Need an coefficient
Location-Link Coefficient Matrix
• For each link, calculate the correlation
between a cell pair (i,j)  ℎ𝑙 ij
• Coefficient Matrix
• When two cell close to each other
– High correlation
• When only one cell affect link l
– Low correlation
Successive Cancellation
• Constructing upper and lower bound
• Iteration
1.
2.
If (energy change < C0 upper bound)  count = 0
Presence detection
1.
If (energy change >= C1 upper bound)
1.
2.
3.
Estimate the occupied cell
Contribution Substracting
1.
5.
Else (goto End)
Cell Identification
1.
4.
Increment count by one, goto next
Substracting
from O
End
1.
2.
If (remained energy change < C1 upper bound)
Increase count
Outline
•
•
•
•
•
Counting multiple subjects
Localizing multiple subjects
Experimental setup and result
Limitation
Conclusion
Conditional Random Field Formulation
• Transition model
• Define
– Cell neighbors: adjacent cells which can be entered
– Order of Neighbor: neighbor distance
– Trajectory ring:
• Radius r: area consist of up to r-order neighbors
• Let Ω𝑟(i) be the cells in i’s r-trajectory
• Nr(i) be the size of Ω𝑟(i) , thus
Localization Algorithm
• Viterbi algorithm: find highest probably path
• Denote Q = {q1,…,qc}, C is total number of subjects
𝐾
• For current state Qt,
permutation
𝐶
• For each permutation, compute Viterbi score
Outline
•
•
•
•
•
Counting multiple subjects
Localizing multiple subjects
Experimental setup and result
Limitation
Conclusion
Experiment Setup
• CC1100 transceiver
– 909.1MHz
– Broadcast 10-byte packet every 0.1s
• RSS collected as a mean value over 1s
• Training phase: 30s in each cell
• Performance metrics
– Counting percentage
– Error distance
Office environment
– 13 transmitter, 9 receiver
– 150 m^2, divided into 37 cell
– Movement scenarios
Counting Percentage
Location-Link Coefficient
Counting Result
Localization Result
Open Floor Space
• 12 transmitter, 8 receiver
• 400 m^2, 56 cells
• Movement scenarios
Location-Link Coefficient
Counting Result
Localization Error
Outline
•
•
•
•
•
Counting multiple subjects
Localizing multiple subjects
Experimental setup and result
Limitation
Conclusion
Limitation
• 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
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•
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•
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Device free localization system
Track multiple subjects
Average 86% counting accuracy ??
Average 1.3m localization accuracy ??
Test in two different environments
– How many iteration?
• Not very successful with more objects