version 1.0.0 scobi introduction tutorial 1
TRANSCRIPT
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Information Processing and Sensing Lab
Version 1.0.0 SCoBi Introduction Tutorial 1
@impress_lab
C O N TA C T
[email protected] impress.ece.msstate.edu
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Information Processing and Sensing Lab
O U T L I N E
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Information Processing and Sensing Lab
Signal of Opportunity Remote Sensing
SIGNALS OF OPPORTUNITY
(SoOp)
Active
Passive
Direct
Reflect
SoOp
A “third way” for remote sensing
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Information Processing and Sensing Lab
Credit: University of Michigan
SoOp Example: CYGNSS
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Information Processing and Sensing Lab
Global CoverageLower Size, Weight,Power, and Cost
(SWaP-C)
SoOp Has Many Advantages for Remote Sensing
SoOp Advantages
Frequent Revisit Time
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Information Processing and Sensing Lab
Surface Soil MoistureRoot-Zone Soil Moisture
TopographySurface Roughness
Vegetation BiomassCanopy Scattering
Snow-Water EquivalentPermafrost
Multilayer ScatteringVolume Scattering
SoOp Faces Many Challenges from Geophysical Parameters!
SoOp Challenges
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Information Processing and Sensing Lab 7
SCoBi – Physics-based Modeling & Simulation
Polarimetric Effects– Fully polarimetric
– Any combination of linear/circular polarization
Configuration Effects
– Altitude
– Spreading loss over vegetation depth
Antenna Effects– Cross-polarization
coupling– Beam divergence– Polarization mixing– Orientation– Beamwidth– Sidelobes
Virtual Vegetation
– Mix vegetation– Growing vegetation– Seasonal effects
InterferometricEffects
– Complex Voltage– Orientation– Beamforming
ignals of Opportunity herent static Scattering Model
Multilayer Effects
– Model complex dielectric media
– Stratified layer division
– Vegetation and subsurface scattering
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Information Processing and Sensing Lab 8
GitHub
SCoBi Currently Available on GitHub!https://github.com/impresslab/SCoBi
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Information Processing and Sensing Lab 9
SCoBi File Structure
UML Files
design docs source
UML-EA manuals input lib sims
User Manual
Developer Manual
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Information Processing and Sensing Lab
SCoBi Simulator Architecture (UML)
1010
Runscobi Behavioral Model SCoBi-Veg Component Model
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Information Processing and Sensing Lab
Blackbox Structure
11
x f(x) y
input lib sims
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Information Processing and Sensing Lab
Required Inputs
12
lib
sims
Antenna Pattern File
Config Inputs File
Veg Inputs File
GUI Window
input
XLSX
XLSX
XLSX
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Information Processing and Sensing Lab
Required Inputs (No Vegetation)
13
lib
sims
Antenna Pattern File
Config Inputs File
Veg Inputs File
GUI Window
input
XLSX
XLSX
XLSX
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Information Processing and Sensing Lab
Required Inputs (No Antenna)
14
lib
sims
Antenna Pattern File
Config Inputs File
Veg Inputs File
GUI Window
input
XLSX
XLSX
XLSX
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Information Processing and Sensing Lab
Required Inputs (No Veg, No Ant)
15
lib
sims
Antenna Pattern File
Config Inputs File
Veg Inputs File
GUI Window
input
XLSX
XLSX
XLSX
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Information Processing and Sensing Lab
Excel Inputs
16
Antenna Pattern FileConfig Inputs File Veg Inputs File
Layers KindsDynamic Ground
gnXX gnXY gnYX gnYY
XLSX XLSX XLSX
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Information Processing and Sensing Lab 17
Simulation Settings
S I M U L AT I O N M O D E
S n a p s h o t o r
T i m e - S e r i e s
G R O U N D C O V E R
V e g e t a t i o no r
B a r e S o i l
G R O U N D S T R U C T U R E
S i n g l e L a y e rO r
M u l t i - l a y e r
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Information Processing and Sensing Lab 18
Simulation Mode Options
TIME-SERIESSNAPSHOT
U s e d f o r a n a l y z i n g s e v e r a l t r a n s m i t t e r , V S M , a n d R M S H c o n f i g u r a t i o n s
U s e d f o r m e a s u r i n g s p e c i f i c t r a n s m i t t e r , V S M , a n d R M S H c o n f i g u r a t i o n s
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Information Processing and Sensing Lab 19
Snapshot Mode Example
Snapshot Mode wi l l compute
combinat ions of transmitter and RMSH conf igurat ions based on the above input .
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Information Processing and Sensing Lab 20
Time-Series Mode Example
Time-Ser ies mode wi l l compute indiv idual conf igurat ions transmitter and RMSH
conf igurat ions based on the above input
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Information Processing and Sensing Lab 21
Ground Cover Options
BARE-SOIL VEGETATION
S o O p r e f l e c t o m e t r y o v e r s u r f a c e s w i t h n o
v e g e t a t i o n
S o O p r e f l e c t o m e t r y o v e r s u r f a c e s w i t h v e g e t a t i o n
l a y e r
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Information Processing and Sensing Lab 22
Ground Structure Options
SINGLE-LAYER MULTI-LAYER
𝜀1
𝜀2
𝜀4
𝜀𝜀3
S o i l i s r e p r e s e n t e d b y a s i n g l e , h o m o g e n o u s
d i e l e c t r i c
S o i l i s r e p r e s e n t e d b y m u l t i p l e l a y e r e d
d i e l e c t r i c s
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Information Processing and Sensing Lab 23
Credit: Alena Koval
O U T L I N E
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Information Processing and Sensing Lab 24
O U T L I N E
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Information Processing and Sensing Lab 25
O U T L I N E
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Information Processing and Sensing Lab
Thank You!
@impress_lab
C O N TA C T
[email protected] impress.ece.msstate.edu
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Information Processing and Sensing Lab
Icon Credits
@impress_lab
C O N TA C T
[email protected] impress.ece.msstate.edu
Stephen Hutchings from the Typicons Set (CC BY 3.0)Eezy from the Technology Devices Set (CC BY 3.0)Icon Minds from the Scarycons SetElizabeth Arostegui from the Technology Mix Set (CC BY 3.0)Perdana Kurniawan Arta from the Fruit and Vegetable Set (CC BY 3.0)Squid.ink from the theSquid.ink set (CC BY 3.0)iconmonstr.com
Alex Smith, PhotoMIX Ltd, Markus Spiske, Jaymantri, Todd Trapani, Pixabay
Photos