Simulated SAR with GIS Data and Pose Estimation using Affine Projection

59 Pages·2017·8.54 MB·English

Simulated SAR with GIS Data
and
Pose Estimation using Affine Projection
Martin Divak
Space Engineering, master's level
2017
Luleå University of Technology
Department of Computer Science, Electrical and Space Engineering

Simulated SAR with GIS Data
and
Pose Estimation using Affine Projection
Author Martin Divak
Thesis supervisor Zoran Sjanic
Examiner Goerge Nikolakopoulos
Co-supervision Christoforos Kanellakis

TheworkpresentedinthisthesiswasconductedatSaabAeronauticsinthesectionSensorFusionandTactical
Control. Interest in the subjects described in this thesis are of interest due to the sections development of
Decision Support Systems for Aircraft applications.

Abstract
Pilots or autonomous aircraft need knowledge of where they are in relation to the environment. On board
aircraft there are inertial sensors that are prone to drift which needs corrections by referencing against a
known item, place, or signal. Satellite data is not always reliable due to natural degradation or intentional
jamming so aircraft are dependant on visual sensors for navigation. Synthetic aperture radar, SAR, is an
interesting candidate as navigation sensor. SAR is a collection of methods used to generate high resolution
radar images using movement to increase its apparent antenna size, or aperture. Radar sensors are not de-
pendant o day light, unlike optical sensors. Infrared sensors can see in the dark but are affected by weather
conditions. Radar sensors active sensors, transmitting pulses and measuring echoes, in the microwave spec-
trum of electromagnetic radiation that does not have strong interactions with meteorological phenomena.
To use radar images in qualitative and quantitative analysis they must be registered with geographical in-
formation. Position data on an aircraft is not sufficient to determine with certainty what or where it is one
is looking at in a radar image without referencing other images over the same area. To lay an image on top
of another image and transforming it such that they match in image content position is called registration.
One way of georeferencing is to simulate a SAR image and register a real image, from the same view, using
corresponding reference points in both images. This present work demonstrate that a terrain model can be
split up and classified into different types of radar scatterers. Different parts of the terrain yielding different
typesofechoesincreasestheamountofradarspecificcharacteristicsinsimulatedreferenceimages. Aterrain
that is relatively flat having to geometric features, may still be used to create simulated radar images for
image matching.
Computer vision with other type of sensors have had a long history compared to radar based systems.
Corresponding methods in radar have not had the same impact. Among these systems that have had a
lot of underlying development include stereoscopic methods where several images are taken of the same
area but from different views, meaning angles and positions, where image depth can be extracted from the
stereo images. Stereoscopic methods in radar image analysis have mainly been used to reconstruct objects
or environments seen from known parallel flight and orbital trajectories. The reverse problem, estimating
positionandattitudegivenaknownterrain,isnotsolved. Thisworkpresentsaninterpretationoftheimaging
geometry of SAR such that existing methods in computer vision may be used to estimate the position from
which a radar image has been taken. This is a direct image matching without requiring registration that is
necessary for other proposals of SAR-based navigation systems. By determination of position continuously
from radar images aircraft could navigate independent of day light, weather, or satellite data.
Page i
Sammanfattning
Piloter eller autonoma flygfarkoster beh¨over k¨annedom om var n˚agonstans de befinner sig i relation till
omgivningen. Ombord p˚a flygfarkoster s˚a finns det tr¨oghetssensorer som p˚averkas av drift vilket beh¨over
korrigeras genom referering mot ett k¨ant f¨orem˚al, plats, eller signal. Satellitdata ¨ar inte alltid p˚alitlig p˚a
grundavnaturligdegraderingelleravsiktligst¨ornings˚a¨arenflygfarkostberoendeavvisuellasensorerf¨oratt
navigera. Syntetiskaperturradar,SAR,¨arenintressantkandidatsomnavigationssensor. SAR¨arensamling
metoder som anv¨ands f¨or att generera h¨oguppl¨osta radarbilder genom att anv¨anda r¨orelse f¨or att ¨oka dess
apparenta antennstorlek, eller apertur. Radarsensorer ¨ar inte beroende av dagsljus som optiska sensorer ¨ar.
Infrar¨oda sensorer kan se i mo¨rker men p˚averkas av v¨aderf¨orh˚allanden som kan blockera infrar¨od str˚alning.
Radarsensorer¨araktivasensorer,skickarpulserochm¨aterekon,imikrov˚agsspektrumetavelektromagnetisk
str˚alning som inte har s¨arskilt starka interaktioner med meteorologiska effekter.
F¨or att kunna anv¨anda radarbilder f¨or kvantitativ s˚av¨al som kvalitativ analys s˚a m˚aste registreras med ge-
ografiskinformation. Positionsdatap˚aenflygfarkost¨arintetillr¨ackligf¨orattkunnabest¨ammameds¨akerhet
vad eller var man ser i en radarbild utan att referera mot andra bilder ¨over samma omr˚ade. Att l¨agga en
bild ovanp˚a en annan och transformera de s˚a att bildinneh˚allets positioner matchar kallas f¨or registrering.
Ett s¨att att g¨ora det p˚a ¨ar att simulera hur en radarbild ser ut, givet att terr¨angen ¨ar k¨and, fr˚an samma vy
f¨or att relatera bildkoordinaterna med v¨arldskoordinater. I detta arbete demonstreras att en terr¨angmodell
kan delas upp och klassificeras som olika typer av radarspridare. Att olika delar av terr¨angen ger olika
ekon ¨okar m¨angden radarspecifik karakteristik i simulerade referensbilder. En terr¨ang som till och med ¨ar
relativt platt, allts˚a inte har n˚agra radarspecifik geometrisk karakteristik, kan ¨and˚a anv¨andas till att skapa
simulerade radarbilder som kan anv¨andas till bildj¨amf¨orelser.
Datorsynmedandratyperavsensorerharenl¨angrehistoriaj¨amf¨ortmedradarbaseradesystem. Motsvarande
metoder inom radar har inte haft lika stort genomslag. Bland dessa system som har haft mycket bakomlig-
gande utveckling inkluderar stereoskopiska metoder d¨ar flera foton tas ¨over samma omr˚ade men fr˚an olika
vyer, allts˚a vinklar och positioner, d¨ar bilddjup kan extraheras fr˚an stereobilderna. Stereoskopiska metoder
inom radarbildanalys har huvudsakligen anv¨ants till att rekonstruera objekt eller omgivningar som ses fr˚an
k¨anda parallela flyg- eller omloppsbanor. Det omv¨anda problemet, estimering av position och attityd givet
en k¨and terr¨ang, har inte en l¨osning. Detta arbete tar upp en tolkning av avbildningsgeometrin s˚a att
existerande metoder inom datorsyn kan nyttjas till att estimera positionen fr˚an vilken en radarbild har
tagits. Detta ¨ar en direktj¨amf¨orelse utan att beh¨ova bildregistrering, som kr¨avs enligt andra f¨orslag p˚a
SAR-baserade Navigationssystem. Genom att kunna best¨amma position kontinuerligt fr˚an radarbilder s˚a
kan flygfarkoster navigera oberoende av dagsljus, v¨ader, och satellitdata.
Page ii
List of Acronyms
AESA Active Electronically Scanned Array
ATR Automatic Target Recognition
BRDF Bidirectional Reflectance Distribution Function
CAD Computer-aided Design
CDT Constrained Delaunay Triangulation
CP Control Point
CPU Central Processing Unit
CV Computer Vision
DEM Digital Elevation Map
DLR Deutsches zentrum fu¨r Luft- und Raumfahrt
DSM Digital Surface Map
DTM Digital Terrain Map
ESA European Space Agency
FOV Field Of View
GCP Ground Control Point
GIS Geographic Information System
GMTI Ground Moving Target Identification
GNSS Global Navigation Satellite System
INS Inertial Navigation System
InSAR SAR Interferometry
KvD Koenderink and van-Doorn
Lidar Light Detecton and Ranging
LOS Line-of-Sight
MTI Moving Target Identification
NLOS Non-Line-of-Sight
Radar Radio Detection and Ranging
RCS Radar Cross-section.
SAR Synthetic Aperture Radar
SLAM Simultaneous Localisation and Mapping
Sonar Sound Navigation and Ranging
UAV Unmanned Aerial Vehicle
Mentioned throughout the thesis are examples of letter designations of electromagnetic spectral bands. The
letterdesignationsoftheelectromagneticspectrumusedinthisthesisfollowsIEEEstandardnomenclature.1
Letter VHF UHF L S C X Ka K Ku V W mm
GHz 0.03-0.3 0.3-1 1-2 2-4 4-8 8-12 12-18 18-27 27-40 40-75 75-110 110-300
HH, VV, and HV represent Transmit-Receive Horizontal/Vertical linear polarization modes.
1IEEEstd521-2002
Page iii
Mathematical Notation
r Slant Range
V¯ Velocity Vector
R¯ Range Vector
f Doppler Centroid Frequency
DC
ω Squint
ω Azimuthal Beamwidth
w
∆t Illumination Time
θ Angular Width in Range/Swath Direction
w
θ Depression Angle
θ Near swath grazing incidence
near
θ Far swath grazing incidence
far
θ Difference in depression angle for parallel stereo channels
diff
C Camera or Intrinsic Matrix
C SAR Intrinsic Matrix
SAR
P Normalized Orthographic Projection Matrix
(cid:107)
P Affine Projection Matrix
Aff
P SAR Projection Matrix
SAR
G Pose or Extrinsic Matrix
G Virtual Orthographic Camera Pose
⊥
R Rotation Matrix
t¯ Translation Vector
u Horizontal Image Centre
0
v Vertical Image Centre
0
c Speed of Light
0
λ Wavelength
δ Slant Range Resolution
r
δ Azimuth Resolution
az
R Bidirectional Reflectance Distribution Function
ϕ Incidence Angle
inc
ϕ Reflection Angle
ref
Page iv