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1 Sensordatafusion Egils Sviestins SaabTech Systems.

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En presentation över ämnet: "1 Sensordatafusion Egils Sviestins SaabTech Systems."— Presentationens avskrift:

1 1 Sensordatafusion Egils Sviestins SaabTech Systems

2 Fusion levels (JDL model) Level 1 Objects Level 1 Objects Level 2 Situations Level 2 Situations Level 3 Intentions Level 3 Intentions Level 4 Process Level 4 Process Sources

3 3 Terminologi Sensordata- fusion Informations- fusion Sensor- data Andra data ObjektSituationerAvsikter Styrning Optimering Styrning Optimering

4 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 5 Från verkligheten... Rån = stöld e.d. som utförs under hot om våld

6 Context

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

8 8 Synkanalen (hypotetiskt!)

9 9 Hörselkanalen (hypotetiskt!)

10 10 WSC Early fusion or late?

11 11 WSC Seeing (hypothetical)

12 12 Artskilda sensorer

13 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 14 Inte så enkelt...

15 15 Fusionsprincip i hjärnan?

16 16 WSC The Radar Data Processing Chain ExtractorReceiverTracker Raw videoPlots (R,az)Tracks (#,x,y,v x,v y,...) A12 A07

17 Steps in Tracking

18 18 WSC The Tracking Cycle

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 t x How to handle maneuvering targets???

21 Alpha-Beta filtering Prediction step Updating step  and  are tuning constants between 0 and 1  : Measurement has no effect  : History has no effect

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

23 Probability densities x x. Prediction Measurement Update

24 IMM States

25 IMM structure

26 26 Bayes teori

27 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

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

29 LPQ association: Plot & Track clusters

30 Bayesian track initiation Given a tentative track. Two hypotheses: H 0 : Track is false H 1 : Track is genuine C n =p(H 1 ): Credibility at scan n Obtained measurement z. Spurious plot density p s.

31 Initiation by Credibility u Required: Fast initiation and low false track rate u Sequential hypothesis testing  Credibility C  likelihood that a potential track is genuine cred C Scan # 0 1

32 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

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 –Sometimes requires hybrid solutions

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 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 Utilise meteorological data if available

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

46 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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