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Sensordatafusion

Sensordatafusion. Egils Sviestins SaabTech Systems. Fusion levels (JDL model). Level 1 Objects. Level 2 Situations. Level 3 Intentions. Sources. Level 4 Process. Terminologi. Objekt. Situationer. Avsikter. Sensordata- fusion. Sensor- data. Informations- fusion. Andra data.

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Sensordatafusion

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  1. Sensordatafusion Egils Sviestins SaabTech Systems

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

  3. Terminologi Objekt Situationer Avsikter Sensordata- fusion Sensor- data 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

  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 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? • 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. C 1 0 1 2 3 4 5 6 8 7 Scan # Initiation by Credibility • Required: Fast initiation and low false track rate • Sequential hypothesis testing • Credibility C » likelihood that a potential track is genuine cred

  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 probabilitiesof possible target types. • Requires a library of target type characteristics

  41. MST+ scenario

  42. Example Lockheed F16 MiG-29 Mirage 2000 Lockheed U2 MiG-25 3 3 3 1 1 3 3 3 3 1 3 3 3 2 2 3 3 3 3 2 3 3 3 4 5 3 3 3 4 5 3 3 3 4 5 6 3 3 3 4 3 3 3 4 5 6 7 6 6 7

  43. 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

  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 CollectedFlight Data NewProbabilityVector [p´(F16),...] PreviousProbabilityVector Bayes’ Rule [p(F16),...] Target TypeDatabase

  46. Avrundning • Sensordatafusion - uppgifter om enskilda objekt baserat (mest) på sensordata • Bygger oftast på matematiska modeller ochBayesiansk 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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