1/23 Indoor Localization Without Infrastructure Using the Acoustic Background Spectrum Stephen P. Tarzia * Peter A. Dinda * Robert P. Dick † Gokhan Memik * * Northwestern University, EECS Dept. † University of Michigan, EECS Dept. Presented at MobiSys 2011 Bethesda, MD, USA June 30, 2011 http://empathicsystems.org
31
Embed
Indoor Localization Without Infrastructure Using the Acoustic ...5/23 Related Work: mobile acoustic sensing M. Azizyan, I. Constandache, and R.R. Choudhury. SurroundSense: mobile phone
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
1/23
Indoor Localization Without InfrastructureUsing the Acoustic Background Spectrum
Definition: indoor localization without infrastructure
Given:
X A smartphone
X A building composed of many rooms
X At least one prior visit to each room for training
Without:
× Specialized hardware
× Anything installed in the environment
× Cooperation from the building owner
Goal:
I Determine which room the smartphone is currently located in
4/23
Summary
Motivation:
I Indoor localization is important
I Wi-Fi is imperfect and not always available
I Improved accuracy is desired
Distinctive elements of our method:
I Listen to background sounds
I Look at frequency domain
I Rank-order filter for noise
Results:
I 69% accuracy for 33 rooms using sound alone
I Publicly-available app
I Effectively combined Wi-Fi and sound
5/23
Related Work: mobile acoustic sensing
M. Azizyan, I. Constandache, and R.R. Choudhury.SurroundSense: mobile phone localization via ambiencefingerprinting. MobiCom’09.
I Characterized rooms by loudness distribution
I Did not use sound exclusively
H. Lu, W. Pan, N.D. Lane, T. Choudhury, and A.T. Campbell.SoundSense: scalable sound sensing for people-centric applicationson mobile phones. MobiSys’09.
I Focused on transient sounds
I Activity detection, not localization
6/23
Acoustic Background Spectrum (ABS)
A location fingerprint should be:
I Distinctive
I rEsponsive
I Compact
I Efficiently-computable
I Noise-robust
I Time-invariant
X 69% matching accuracy
X 4–30 second sample
X ∼1 kB per fingerprint
X ∼12% mobile CPU usage
∼ sometimes can adapt
X tested on different days
6/23
Acoustic Background Spectrum (ABS)
A location fingerprint should be:
I Distinctive
I rEsponsive
I Compact
I Efficiently-computable
I Noise-robust
I Time-invariant
X 69% matching accuracy
X 4–30 second sample
X ∼1 kB per fingerprint
X ∼12% mobile CPU usage
∼ sometimes can adapt
X tested on different days
7/23
Signal Processing
Discard rows > 7 kHz
Record audio samples
Divide samples into frames
Compute power spectrum of each frame
time
time
fre
q.
Sort each remaining row
spectrogram
audio sample time series
fre
q.
increasing magnitude Extract 5th percentile columnand take logarithm
[ ]= Acoustic Background Spectrum
microphone input
* * * * * * * * * * * * * * * * * *Multiply frames by a window function
Sta
nd
ard
sp
ectr
al a
nal
ysis
AB
S f
ing
erp
rin
t ex
trac
tio
n
8/23
ABS Fingerprints
Various rooms
10-2
100
102
104
106
108 Room 15
10-2
100
102
104
106
108
norm
aliz
ed, lo
g-s
cale
ener
gy
Room 16
10-2
100
102
104
106
108
0 1 2 3 4 5 6 7
frequency (kHz)
Room 17
Different positions and days
10-2
100
102
104
106
108 Room 15
10-2
100
102
104
106
108
norm
aliz
ed, lo
g-s
cale
ener
gy
Room 16
10-2
100
102
104
106
108
0 1 2 3 4 5 6 7
frequency (kHz)
Room 17
8/23
ABS Fingerprints
Various rooms
10-2
100
102
104
106
108 Room 15
10-2
100
102
104
106
108
norm
aliz
ed, lo
g-s
cale
ener
gy
Room 16
10-2
100
102
104
106
108
0 1 2 3 4 5 6 7
frequency (kHz)
Room 17
Different positions and days
10-2
100
102
104
106
108 Room 15
10-2
100
102
104
106
108
norm
aliz
ed, lo
g-s
cale
ener
gy
Room 16
10-2
100
102
104
106
108
0 1 2 3 4 5 6 7
frequency (kHz)
Room 17
9/23
Experimental Platforms
(a) Zoom H4n (b) Apple iPod Touch
10/23
Experimental Rooms
0
5
10
15
20
officelounge
computer lab
classroomlecture hall
Inst
ance
s
Room type
0 2 4 6 8
10 12 14 16
4 8 16 32 64 128 256 512
Inst
ance
s
Maximum room capacity
11/23
Fingerprint-based localization
Supervised learning with two phases:
I Training – gather labeled fingerprints
I Testing/operation – observe new, unlabeled fingerprints
I Experiments use leave-one-out simulation
Our classifier:
I Euclidean distance metric for comparing fingerprints(equivalent to RMS error)
I Nearest-neighbor classification
In summary
To guess the current location find the “closest” fingerprint in adatabase of labeled fingerprints.
12/23
Accuracy Scaling
0 10 20 30 40 50 60 70 80 90
100
2 4 8 17 33
Acc
ura
cy (
%)
Number of rooms in database (log scale)
Proposed Acoustic Background SpectrumSurroundSense [Azizyan et al.]
Random chance
I SurroundSense is used in a way not intended by the authors:using the microphone alone
13/23
ABS Parameters
Presented now:
I Filter rank
I Listening time
I Fingerprintsize/resolution
In paper:
I Frequency band
I Distance metric
I Spectrogram window
14/23
Rank-order Filtering
0
20
40
60
80
100
meanmin
p05p10
p25median
p95max
Acc
ura
cy (
%) Fingerprint type
standard spectrumproposed rank-order filtered spectrum
I 33 rooms in database
I Rank-order filters outperforms simple mean⇒ our transient noise filtering technique is effective
15/23
Listening time
0
10
20
30
40
50
60
70
80
1 2 4 8 15 30
Acc
ura
cy (
%)
Sample time, in seconds
16/23
Frequency resolution
0
10
20
30
40
50
60
70
80
1 10 100 1000
Acc
ura
cy (
%)
Frequency bins / fingerprint vector length
17/23
Batphone app in iTunes store
I Uses a 10 secondsliding window
I Streaming signalprocessing
I Combines Wi-Fi withacoustic fingerprint
18/23
Batphone results
0
20
40
60
80
100
Linear combination
ABSCommercial W
i-Fi
Random
Acc
ura
cy (
%)
Batphone localization accuracy
proposed methods
I 43 rooms in databaseI Similar ABS accuracy for iPod and audio recorderI Linear combination of Wi-Fi and ABS works wellI Didn’t compare to state-of-the-art Wi-Fi localization
19/23
Orthogonality of Wi-Fi and Acoustics
2D histograms of physical and fingerprint distances
0 20 40 60 80
100 120 140 160
0 10 20 30 40 50 60
Wi-
Fi
dis
tan
ce (
m)
Real physical distance (m)
0 100 200 300 400 500 600 700 800
0 10 20 30 40 50 60
AB
S d
ista
nce
(d
B)
I Wi-Fi fingerprints from distant rooms are always different
I ABS fingerprints from nearby rooms can be quite different