Upload
felix
View
55
Download
0
Tags:
Embed Size (px)
DESCRIPTION
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 - PowerPoint PPT Presentation
Citation preview
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