Information Processing and Sensing Lab

Version 1.0.0 SCoBi Introduction Tutorial 1

@impress_lab

C O N TA C T

impress@ece.msstate.edu impress.ece.msstate.edu

Information Processing and Sensing Lab

O U T L I N E

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

Information Processing and Sensing Lab

Credit: University of Michigan

SoOp Example: CYGNSS

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

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

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

Information Processing and Sensing Lab 8

GitHub

SCoBi Currently Available on GitHub!https://github.com/impresslab/SCoBi

Information Processing and Sensing Lab 9

SCoBi File Structure

UML Files

design docs source

UML-EA manuals input lib sims

User Manual

Developer Manual

Information Processing and Sensing Lab

SCoBi Simulator Architecture (UML)

1010

Runscobi Behavioral Model SCoBi-Veg Component Model

Information Processing and Sensing Lab

Blackbox Structure

11

x f(x) y

input lib sims

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

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

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

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

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

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

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

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 .

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

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

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

Information Processing and Sensing Lab 23

Credit: Alena Koval

O U T L I N E

Information Processing and Sensing Lab 24

O U T L I N E

Information Processing and Sensing Lab 25

O U T L I N E

Information Processing and Sensing Lab

Thank You!

@impress_lab

C O N TA C T

impress@ece.msstate.edu impress.ece.msstate.edu

Information Processing and Sensing Lab

Icon Credits

@impress_lab

C O N TA C T

impress@ece.msstate.edu 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