Egils Sviestins SaabTech Systems

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1 Egils Sviestins SaabTech Systems
Sensordatafusion Egils Sviestins SaabTech Systems

2 Fusion levels (JDL model)
Objects Level 2 Situations Level 3 Intentions Sources Level 4 Process

3 Terminologi Objekt Situationer Avsikter Sensordata- fusion Sensor-
Informations- fusion Andra data Styrning Optimering

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 1 2 3

5 Från verkligheten... Rån = stöld e.d. som utförs under hot om våld

6 Context This is about multitarget, multisensor tracking.
Sensors like radars, jam strobe detectors, ESM equipment, IRST, GPS, pressure based altimeters, observe targets, and report measurements to command & control centre. Measurements from radars often called plots. Measurements from passive sensors often called strobes. Platforms may be moving, although not tested in real life. Tracker maintains a track for each target based on correlated measurements.

7 Data processing: Improvement or Destruction?
Raw information Sensor User Meaningful information

8 Synkanalen (hypotetiskt!)

9 Hörselkanalen (hypotetiskt!)

10 Early fusion... ... or late? WSC

11 Seeing (hypothetical)
WSC

12 Artskilda sensorer

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?

16 The Radar Data Processing Chain
Receiver Extractor Tracker A12 A07 Raw video Plots (R,az) Tracks (#,x,y,vx,vy,...) WSC

17 Steps in Tracking

18 The Tracking Cycle WSC

19 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?

20 Linear regression x How to handle maneuvering targets??? t

21 Alpha-Beta filtering Prediction step Updating step
a and b are tuning constants between 0 and 1 Prediction step Updating step a=b=0: Measurement has no effect a=b=1: History has no effect

22 Kalman filtering Current state & uncertainties +
Measurement & uncertainties = New state & uncertainties Like a-b-filter, but: Automatically optimizes a and b Best weighting between history and measurement Output includes estimated accuracy

23 Probability densities
. x Update Prediction Measurement x

24 IMM States

25 IMM structure

26 Bayes teori

27 Associering M målspår, N plottar: hur koppla samman? Närmaste granne?
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

28 Measurement-to-track association
Clusters with M measurements and N tracks Form hypotheses like Calculate probabilities for each hypothesis, e.g.

29 LPQ association: Plot & Track clusters

30 Bayesian track initiation
Given a tentative track. Two hypotheses: H0: Track is false H1: Track is genuine Cn=p(H1): Credibility at scan n Obtained measurement z. Spurious plot density ps.

31 Initiation by Credibility
Required: Fast initiation and low false track rate Sequential hypothesis testing Credibility C » likelihood that a potential track is genuine C 1 2 3 4 5 6 7 8 Scan # cred

32 Andra sensorer Bildalstrande Icke bildalstrande TV
FLIR (Forward Looking Infrared) Millimetervågsradar SAR (Synthetic Aperture Radar) Icke bildalstrande Störbäringsavtagare Signalspaning IRST (Infrared Search & Track) Akustiska/Hydroakustiska sensorer GPS

33 Decentralized Multi-Radar Tracking

34 Centralized Multi-Radar Tracking

35 Filling coverage gaps Two radars Coverage gap Red single radar track
lost and reinitiated Decentralized MRT may give confusing picture Centralized MRT performs well

36 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

37 Strobes only 150 km

38 Crossings

39 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

40 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

41 MST+ scenario

42 Example Lockheed F16 Mirage 2000 Lockheed U2 MiG-25 MiG-29

43 Kinematic typing Offline: 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

44 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

45 Step 2 - Update Probability Vector
Collected Flight Data New Probability Vector [p´(F16),...] Previous Probability Vector Bayes’ Rule [p(F16),...] Target Type Database

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