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WINLAB
SCPL: Indoor Device-Free Multi-Subject Counting and Localization Using
Radio Signal Strength
Rutgers University
Chenren Xu
Joint work with Bernhard Firner, Robert S. Moore, Yanyong Zhang
Wade Trappe, Richard Howard, Feixiong Zhang, Ning An
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Device-free Localization
2
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Device-free Localization
3
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Why Device-free Localization?
q Monitor indoor human mobility
4
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Why Device-free Localization?
q Monitor indoor human mobility
Elder/health care 5
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Why Device-free Localization?
q Monitor indoor human mobility
Traffic flow statistics 6
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Why Device-free Localization?
q Monitor indoor human mobility
Traffic flow statistics 7
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Why Device-free Localization?
q Monitor indoor human mobility
q Health/elder care, safety
q Detect traffic flow
q Provides privacy protection
q No identification
8
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Why Device-free Localization?
q Monitor indoor human mobility
q Health/elder care, safety
q Detect traffic flow
q Provides privacy protection
q No identification
q Use existing wireless infrastructure
9
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Previous Work
q Single subject localization
q Fingerprinting-based approach
10
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Fingerprinting N Subjects
q Multiple subjects localization
q Needs to take calibration data from N people
for localizing N people
11
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Fingerprinting N Subjects
12
…
9 trials in total for 1 person
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Fingerprinting N Subjects
13
…
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Fingerprinting N Subjects
14
…
…
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Fingerprinting N Subjects
15
…
…
36 trials in total for 2 people!
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Fingerprinting N Subjects
16
1 person
9 cells 9
9 × 1 min = 9 min
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Fingerprinting N Subjects
17
1 person 2 people
9 cells 9 36
36 cells 36 630
630 × 1 min = 10.5 hr
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Fingerprinting N Subjects
18
1 person 2 people 3 people
9 cells 9 36 84
36 cells 36 630 7140
100 cells 100 4950 161700
161700 × 1 min = 112 days The calibration effort is prohibitive !
WINLAB
SCPL q Input
q Collecting calibration data only from 1 subject (D1)
q Observed RSSI change caused by n subjects
q Output
q count and localize N subjects.
q Main Insight:
q If the number n is known, localizing n subjects
is strightforward 19
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No Subjects
20
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One Subject
21
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Two Subjects
22
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Measurement
23
N = 0 N = 1 N = 2 Link 1 0 4 4 Link 2 0 5 7 Link 3 0 0 5 Total (∆N) 0 9 16
∆N N?
∆N / ∆1 = N?
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Measurement
24
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Measurement
25
Nonlinear problem!
1.6
∆N / ∆1 ≠ N
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Measurement
26
4 dB
5 dB
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Measurement
27
5 dB
6 dB
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Measurement
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4 dB + 0 dB = 4 dB √ 5 dB + 6 dB = 11 dB ≠ 7 dB X 0 dB + 5 dB = 5 dB √
= + ?
4 dB
5 dB
5 dB
6 dB
5 dB
7 dB
4 dB
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Measurement
29
5 dB + 6 dB ≠ 7 dB X
Shared links observe nonlinear fading effect from multiple people
≠ + ! 5 dB 6 dB 7 dB
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SCPL Part I Sequential Counting (SC)
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Counting algorithm
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Phase 1: Detection
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Measurement in 1st round
5 dB
7 dB
4 dB ∆N = 4 + 7 + 5 = 16 dB
∆N > ∆1
Subject Count ++
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Phase 2: Localization
33
Measurement in 1st round
5 dB
7 dB
4 dB
Find this guy
PC-DfP:
C. Xu, B. Firner, Y. Zhang, R. Howard, J. Li, and X. Lin. Improving rf-based device-free passive localization in cluttered indoor environments through probabilistic classification methods. In Proceedings of the 11th international conference on Information Processing in Sensor Networks, IPSN ’12
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Phase 3: Subtraction
34
Calibration data
5 dB
6 dB
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Phase 3: Subtraction
35
Subject count ++ Go to the next iteration…
= -
Measurement in 1st round
Calibration data
Measurement In 2nd round
4 dB
1 dB
5 dB
6 dB
5 dB
7 dB
4 dB
WINLAB
Phase 3: Subtraction
36
Subject count ++ Go to the next iteration…
Hold on …
= -
Calibration data
Measurement in 1st round
Measurement In 2nd round
4 dB
1 dB
5 dB
6 dB
5 dB
7 dB
4 dB
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Phase 3: Subtraction
37
Measurement In 2nd round
4 dB
1 dB
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Phase 3: Subtraction
38
Measurement In 2nd round
Calibration data
4 dB
1 dB
4 dB
5 dB
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Phase 3: Subtraction
39
Measurement In 2nd round
Calibration data
= -
We over-subtracted its impact on shared link!
4 dB
1 dB
4 dB
5 dB -4 dB
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Measurement
40
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Measurement
41
1st round
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Measurement
42
1st round
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Measurement
43
1st round
2st round
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Phase 3: Subtraction
44
We need to multiply a coefficient β ϵ [0, 1] when subtracting each link
= -
Calibration data
Measurement in 1st round
Measurement In 2nd round
4 dB
1 dB
5 dB
6 dB
5 dB
7 dB
4 dB
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Location-Link Correlation
q To mitigate the error caused by this over-
subtraction problem, we propose to
multiply a location-link correlation
coefficient before successive subtracting:
45
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Phase 3: Subtraction
46
Subject count ++ Go to the next iteration…
= - 4.6 dB 6 × 0.4 dB
Measurement in 1st round
Calibration Data
Measurement in 2nd round
5 × 0.8 dB
4 dB
5 dB
7 dB
4 dB
1 dB
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Phase 3: Subtraction
47
Measurement in 2nd round
Calibration data
= -
Measurement in 3rd round
We are done !
4.6 dB
4 dB
1 dB
6 × 0.6 dB
4 × 0.8 dB 1 dB
1 dB
1 dB
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SCPL Part II Parallel Localization (PL)
48
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Localization
q Cell-based localization
q Allows use of context information
q Reduce calibration overhead
q Classification problem formulation
49
C. Xu, B. Firner, Y. Zhang, R. Howard, J. Li, and X. Lin. Improving rf-based device-free passive localization in cluttered indoor environments through probabilistic classification methods. In Proceedings of the 11th international conference on Information Processing in Sensor Networks, IPSN ’12
WINLAB
Linear Discriminant Analysis q RSS measurements with person’s presence in each
cell is treated as a class/state k
q Each class k is Multivariate Gaussian with common
covariance
q Linear discriminant function:
50
Link 1 RSS (dBm) L
ink
2 R
SS (d
Bm
) k = 1 k = 2
k = 3
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Localization
q Cell-based localization
q Trajectory-assisted localization
q Improve accuracy by using human mobility
constraints
51
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Human Mobility Constraints
52
You are free to go anywhere with limited step size inside a ring in free space
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Human Mobility Constraints
53
In a building, your next step is constrained by cubicles, walls, etc.
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Phase 1: Data Likelihood Map
54
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Impossible movements
55
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Impossible movements
56
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Phase 2: Trajectory Ring Filter
57
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Phase 3: Refinement
58
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Here you are!
59
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Viterbi optimal trajectory
q Single subject localization
q Multiple subjects localization
60
ViterbiScore =
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System Description
q Hardware: PIP tag
q Microprocessor: C8051F321
q Radio chip: CC1100
q Power: Lithium coin cell battery
q Protocol: Unidirectional heartbeat (Uni-HB)
q Packet size: 10 bytes
q Beacon interval: 100 msec 61
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Office deployment
62 Total Size: 10 × 15 m
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Office deployment
63 37 cells of cubicles, aisle segments
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Office deployment
64 13 transmitters and 9 receivers
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Office deployment
65 Four subjects’ testing paths
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Counting results
66
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Counting results
67
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Localization results
68
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Open floor deployment
69 Total Size: 20 × 20 m
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Open floor deployment
70 56 cells, 12 transmitters and 8 receivers
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Open floor deployment
71 Four subjects’ testing paths
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Counting results
72
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Localization results
73
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Conclusion and Future Work q Conclusion
q Calibration data collected from one subject can be used to
count and localize multiple subjects.
q Though indoor spaces have complex radio propagation
characteristics, the increased mobility constraints can be
leveraged to improve accuracy.
q Future work
q Count and localize more than 4 subjects 74
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Q & A
Thank you
75