Simulation for a volcano
monitoring network
Rainer Mautz
ETH Zurich, Institute of Geodesy and Photogrammetry
November 22nd, 2008
Session 9: Natural hazards and risks
Motivation
Positioning Algorithm
Contents
1. Motivation
2. Positioning Algorithm
3. Simulation Setup
4. Simulation Results
5. Conclusion & Outlook
Simulation Setup
Simulation Results
Conclusions & Outlook
Motivation
Positioning Algorithm
Simulation Setup
Simulation Results
Conclusions & Outlook
1. Motivation
Volcanoes experience pre-eruption surface deformation
Reason: internal magma pressure cause surface bulge
displacements
 direction:
upwards and outwards
 horizontal:
radial pattern up to 10 cm
 vertical:
uplift of 4 - 6 cm / year (typical)
 area:
over 10 km2
goal
Mount St. Helens, Washington
 spatially distributed position based monitoring system for early warning
 positioning for spatio-temoral referencing of additional sensors
e.g. seismicity, geothermal, gravity, geomagnetic data
Motivation
Positioning Algorithm
Simulation Setup
Simulation Results
Conclusions & Outlook
1. Motivation
 SAR interferometry: update rate 35 days
 Geodetic GNSS:
expensive, energy consuming
Feasibility of a positioning system with deployed location aware sensor nodes
 tiny nodes
 low cost
 battery-powered
 self positioning
 ranging capability
 high density
short range – low power
Motivation
Positioning Algorithm
Simulation Setup
Simulation Results
Conclusions & Outlook
1. Motivation
GPS (anchor nodes)
tiny nodes
inter-node distances
Tiny Node
GPS Station
Motivation
Positioning Algorithm
Simulation Setup
Simulation Results
Conclusions & Outlook
2. Positioning Algorithm
Principle of Wireless Positioning: Multi-Lateration
known node
unknown node
range measurement
Motivation
Positioning Algorithm
Simulation Setup
Simulation Results
Conclusions & Outlook
2. Positioning Algorithm
Iterative Multi-Lateration:
Initial anchors
Step 1:
becomes anchor
Step 2:
becomes anchor
Step 3:
becomes anchor
Motivation
Positioning Algorithm
Simulation Setup
Simulation Results
Conclusions & Outlook
2. Positioning Algorithm
Ambiguity problem when creating the smallest rigid structure
(a)
(b)
5
3
1
4
2
(c)
5
3
1
3
2
1
4
4
5’
2
Motivation
Positioning Algorithm
Simulation Setup
Simulation Results
Conclusions & Outlook
2. Positioning Algorithm
Positioning Strategy
input ranges
return refined coordinates
and standard variations
Creation of a robust structure
find 5 fully
connected
nodes
failed
achieved
failed
volume test
achieved
failed
ambiguity test
achieved
assign local coordinates
free LS adjustment
Coarse Positioning
Transformation into a reference
system
yes
anchor
nodes
available?
input anchor
nodes
no
return local
coordinates
Merging of Clusters
(6-Parameter Transformation)
Expansion of minimal structure
(iterative multilateration)
Motivation
Positioning Algorithm
Simulation Setup
Simulation Results
Conclusions & Outlook
3. Simulation Setup
Object of study: Sakurajima
Stratovolcano, summit with three peaks, island 77 km2
1117 m height
extremely active: strombolian, plinian
densely populated: Kagoshima, 680.000
on island 7.000
monitored by Sakurajima Volcano Observatory
(levelling, EDM, GPS)
Landsat image, created by NASA
Motivation
Positioning Algorithm
Simulation Setup
Simulation Results
Conclusions & Outlook
3. Simulation Setup
Sakurajima Mountain – Digital Surface Model
10 x 10 m grid
Central part of volcano
Area 2 km x 2.5 km
Data provided by Kokusai Kogyo Co. Ltd
Motivation
Positioning Algorithm
Simulation Setup
Simulation Results
Conclusions & Outlook
3. Simulation Setup
Parameters for Simulation
Parameter
Default Value
Range
Number of tiny nodes
400
100 – 1000
Number of GPS nodes (anchors)
10
1–5%
400 m
200 – 500 m
10
4 - 12
1 cm
0–1m
Maximum range (radio link)
Inter-nodal connectivity
Range observation accuracy
Node distribution
grid / optimised
Motivation
Positioning Algorithm
Simulation Setup
Simulation Results
Conclusions & Outlook
4. Simulation Results
400 nodes on a 100 m x 125 m grid. 1838 lines of sight with less than 500 m
Motivation
Positioning Algorithm
Simulation Setup
Simulation Results
4. Simulation Results
Optimised positions. 5024 lines of sight with less than 500 m
Conclusions & Outlook
Motivation
Positioning Algorithm
Simulation Setup
Simulation Results
Conclusions & Outlook
4. Simulation Results
Maximum radio range versus
number of range measurements
Maximum radio range versus
number of positioned nodes
30
400
connected nodes
ranges per node
25
20
15
200
100
10
5
200
300
250
300
350
400
maximal range [m]
450
500
0
200
250
300
350
400
maximal range [m]
450
500
Motivation
Positioning Algorithm
Simulation Setup
Simulation Results
Conclusions & Outlook
4. Simulation Results
Number of located nodes in dependency of the number of anchor nodes
Number of
anchors
Anchor
fraction
Number of
located nodes
Success rate
Number of
ranges
3
0.8 %
3
1%
3
5
1.2 %
191
48 %
3556
10
2.5 %
354
88 %
4553
15
3.8 %
371
93 %
4874
20
5.0 %
400
100 %
5024
Motivation
Positioning Algorithm
Simulation Setup
Simulation Results
Conclusions & Outlook
4. Simulation Results
Correlation between Ranging Error and Positioning Error
mean errors / deviation [m]
4
3.5
3
2.5
2
1.5
1
+ true deviation
● mean error (as result of adjustment)
0.5
0
0
0.2
0.4
0.6
noise [m]
0.8
1
Motivation
Positioning Algorithm
Simulation Setup
Simulation Results
Conclusions & Outlook
4. Simulation Results
Mean errors of the X- Y- and Z-components sorted by the mean 3D
point errors (P)
0.16
0.14
mean error [m]
0.12
X
Y
Z
P
0.1
0.08
0.06
0.04
0.02
0
0
100
200
node number
300
400
Motivation
Positioning Algorithm
Simulation Setup
Simulation Results
5. Conclusions
 Feasibility of a wireless sensor network shown
 Direct line of sight requirement difficult to achieve
 10 % GPS equipped nodes required
 Error of height component two times larger
 Position error ≈ range measurement error
Outlook
 Precise ranging (cm) between networks to be solved
 Protocol & power management
Conclusions & Outlook
Motivation
Positioning Algorithm
Simulation Setup
End
Simulation Results
Conclusions & Outlook