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

• 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

• 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) 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

• Iteration1. If (energy change < C0 upper bound) count = 02. Presence detection

1. If (energy change >= C1 upper bound)1. Increment count by one, goto next

2. Else (goto End)

3. Cell Identification1. Estimate the occupied cell

4. Contribution Substracting1. Substracting from O

5. End1. If (remained energy change < C1 upper bound)2. 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 be the cells in i’s r-trajectory• Nr(i) be the size of , thus

Localization Algorithm

• Viterbi algorithm: find highest probably path

• Denote Q = {q1,…,qc}, C is total number of subjects• For current state Qt, permutation• 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

• 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