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Sensordatafusion
Egils SviestinsSaabTech Systems
Fusion levels (JDL model)
Level 1ObjectsLevel 1Objects
Level 2SituationsLevel 2
SituationsLevel 3
IntentionsLevel 3
Intentions
Level 4ProcessLevel 4Process
Sources
3
Terminologi
Sensordata-fusion
Informations-fusion
Sensor-data
Andradata
Objekt Situationer Avsikter
StyrningOptimering
StyrningOptimering
4
Modeller
• Mätningar/information räcker inte
• Modeller krävs!• Matematiska:
– exempel
• Idéer om verkligheten/”mentala” modeller– Begränsat av naturlagar, ekonomiska lagar, mänsklig förmåga etc.
• Mätningar/information snävar in möjligheterna
an)bruspåverk med el(kastparab )(2
2
tdtd
wgx
+=
12
3
5
Från verkligheten...
Rån = stöld e.d. som utförs under hot om våld
Context
Data processing: Improvement or Destruction?
Raw information
Meaningful information
Sensor User
8
Synkanalen (hypotetiskt!)
TolkningLinjerYtor
Pixels Linjer, ytor Fysiska kroppar
Extraktionav kroppar
Erfarenhet
Begrepp
9
Hörselkanalen (hypotetiskt!)
Tolkning
Erfarenhet
TonerTransienter
FrekvensAmplitud
TonerTransienter Ljudkällor Ord mm.
410Hz, 63 dBk Mänsklig
röst Kaffeaaaf
Sortering
10WSC
Early fusion...
... or late?
11WSC
Seeing (hypothetical)
PixelsLinesSurfaces
Physicalbodies
Knownobjects
Pixels LinesSurfaces
12
Artskilda sensorer
Kaffedags
Kaffekask
Kaffetax
??
13
Tidig fusion - för och emot
• Mindre risk för tvetydigheter• Osäkerheter kan lättare beskrivas statistiskt - Bayes teori
kan användas
• Mindre robust m a p systematiska fel• Svårt hantera artskilda källor
14
Inte så enkelt...
15
Fusionsprincip i hjärnan?
16WSC
The Radar Data Processing Chain
ExtractorReceiver Tracker
Raw video Plots (R,az) Tracks (#,x,y,vx,vy,...)
A12
A07
Steps in Tracking
18WSC
The Tracking Cycle
Pred iction
Ass
ocia
tion Updating
Initiation
TerminationMeasurements
Filtering techniques
• Linear regression (least squares batch processing) (hardly used in this context)
• (70’s) Alpha-Beta• (80’s) Adaptive Kalman• (90’s) Interactive Multiple Model (IMM) • (2000’s ?) Non-linear filtering?
Linear regression
t
x
How to handlemaneuveringtargets???
Alpha-Beta filtering~, &~ ,
$ , &$ ,
x x
x xx
predicted position speed
updated position speedmeasured positionm
~ $ &$
&~ &$x x x
x x
= +
=
T
( )$ ~ ~
&$ &~~
x x x x
x xx x
= + −
= +−
α
β
m
m
T
Prediction step
Updating step$, &$x x
~, &~x x
new ifand
$ , &$
. .x x
α β= =0 5 0 5
xm
α and β are tuning constantsbetween 0 and 1
α=β=0: Measurement has no effectα=β=1: History has no effect
Kalman filtering
Like a-b-filter, but:Automatically optimizes a and bBest weighting between history
and measurementOutput includes estimated accuracy
Current state & uncertainties+
Measurement & uncertainties=
New state & uncertainties
Probability densities
x
x.
Prediction
Measurement
Update
IMM States
&&&& , &&x l
x s x tl s l t s t
⋅ =
⋅ ⋅ =⋅ = ⋅ = ⋅ =
white noise
white noise0
&& ; &x x uu
= ⋅ =0 0= vertical unit vector
Dynamics
&& &&
&& ( &
x u x llx u x
⋅ ⋅ ==⋅ ×
and white noise longitudinal unit vector
) = 0
&ωω
==
0 turn rate
( , , , & , & , & )x x x x x x1 2 3 1 2 3
Linear Kalman filter
( , , , & , & , & , )x x x x x xnd
1 2 3 1 2 3
2
ω
order Extended Kalman
( , , , & , & , & )x x x x x x1 2 3 1 2 3
Linear Kalman filter
State Vector and Filter Type
( , , , & , & , & )x x x x x x1 2 3 1 2 3
Linear Kalman filterUniformHorizontal Motion
Speed Changes
Slow Turns
Fast Maneuvers
IMM structure
Input
Transition
Merging
Propagation
Updating
Averaging& Output
UH
UH UH UH UH
UH
UH
UH
X
SC ST FM
SC SC SC SC
SC
SC
SC
ST ST ST ST
ST
ST
ST
FM FM FM FM
FM
FM
FM
26
Bayes teori
p(H )
p(H )
p(H )
1
2
3
p'(H )
p'(H )
p'(H )
1
2
3
Observation z
∝ i
i
p'(H )
p(z|H )p(H )i
27
Associering
• M målspår, N plottar: hur koppla samman?– OBS! Falska/saknade plottar, falska/saknade målspår
• Närmaste granne?• Närmaste granne i statistiskt avstånd?• Global optimering statistiskt avstånd
(minimera )?• Söka globalt mest sannolika koppling?
Hur man än gör kan det bli fel. Motiverar multihypotes
∑ 2d
• Clusters with M measurements and N tracks• Form hypotheses like
• Calculate probabilities for each hypothesis, e.g.
Measurement-to-track association
( ) ( )P p z x p P p z x Pd s d d1 3 3 2 1( )−
H z x z z x x( , , , )1 3 2 3 2 1→ → ∅ → ∅ →
LPQ association: Plot & Track clusters
∗
∗
∗∗
∗∗ ∗
∗
∗
∗
∗
Track predictedposition andsearch bin
Plot
Cluster with3 plots and 2 tracks
OH103
Bayesian track initiation
Given a tentative track. Two hypotheses:H0: Track is falseH1: Track is genuineCn=p(H1): Credibility at scan n
Obtained measurement z. Spurious plot density ps.
( ) ( )( ) ( )[ ]
p H z C
p z H p H
P p z x P p C
n
d d s n
( )1 1
1 1
1
=
∝
= + −
+ ( ) ( )( )
p H z p z H p H
p Cs n
( )0 0 0
1
∝
= −
Initiation by Credibility
uRequired: Fast initiation and low false track rateu Sequential hypothesis testinguCredibility C ≈ likelihood that a potential track is
genuine
cred
C
1 2 3 4 5 6 7 8 Scan #
0
1
32
Andra sensorer
• Bildalstrande– TV– FLIR (Forward Looking Infrared)– Millimetervågsradar– SAR (Synthetic Aperture Radar)
• Icke bildalstrande– Störbäringsavtagare– Signalspaning– IRST (Infrared Search & Track)– Akustiska/Hydroakustiska sensorer– GPS
Decentralized Multi-Radar Tracking
Tracking
TrackingPlots
Plots
Trackcorrelation& merging
System tracks
Centralized Multi-Radar Tracking
System tracks
Multi-radartracking
Plots
Plots
Filling coverage gaps1. 2.
3. 4.
Two radarsCoverage gap Red single
radar tracklost andreinitiated
DecentralizedMRT may giveconfusing picture
Centralized MRTperforms well
Disadvantages of centralized multi-radar tracking
• More sensitive to bias errors– Bias compensation required
• Difficult to distribute CPU load on several processors– But not impossible
• Existing data links often do not supply plot level data– Sometimes requires hybrid solutions
• Sensors sometimes include extensive processing– Sometimes requires hybrid solutions
Strobes only
150 km
Crossings
Reasons for Multi-Sensor Tracking
• Radars can be jammed• Protective need to keep radars silent• Radars don’t always give best target detection• May support target identification
Target Type Identification
• Based on– Direct observations– ESM / IRST measurements– Kinematics
• Each track carries a vector with probabilities of possible target types.
• Requires a library of target type characteristics
MST+ scenario
42
Example
T1 T2 T 3 T4 T 5
M1 M2 M3 M4 M5 M6 M7
T 1
T 2
T 3
T 4
T 5
3 3 3 1 1 3 3 3 3 13 3 3 2 2 3 3 3 3 23 3 3 4 5 3 3 3 4 53 3 3 4 5 6 3 3 3 43 3 3 4 5 6 7 6 6 7
Lockheed F16Lockheed F16 Mirage 2000Mirage 2000 Lockheed U2Lockheed U2 MiGMiG--2525 MiGMiG--2929
Kinematic typingOffline: Create Target Type Database
• Max altitude• Min/Max speed as function of altitude• Max climb rate as function of altitude• Max distance from base• Max linear/turn acceleration as function of altitude
Step 1 - Collect flight data
• Max altitude• Min/max velocity as function of altitude• Max climb rate• Max distance from base• <Max linear/turn acceleration as function of altitude>• Utilise meteorological data if available
NewProbability
Vector[p´(F16),...]
Step 2 - Update Probability Vector
CollectedFlight Data
Target TypeDatabase
PreviousProbability
Vector[p(F16),...]
Bayes’ Rule
46
Avrundning• Sensordatafusion - uppgifter om enskilda objekt baserat (mest) på
sensordata• Bygger oftast på matematiska modeller och
Bayesiansk hypotesprövning• Många svåra områden återstår
– Sensorer som ger knepiga data– Svårtolkade scenarier (t ex mark och undervatten)– Gemensam lägesbild (distribuerad fusion)– Fusion av starkt artskilda sensorer– Integration med infofusion
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