Egils Sviestins SaabTech Systems Sensordatafusion Egils Sviestins SaabTech Systems

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

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

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

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

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.

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

Synkanalen (hypotetiskt!)

Hörselkanalen (hypotetiskt!)

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

Seeing (hypothetical) WSC

Artskilda sensorer

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

Inte så enkelt...

Fusionsprincip i hjärnan?

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

Steps in Tracking

The Tracking Cycle WSC

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 x How to handle maneuvering targets??? t

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

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

Probability densities . x Update Prediction Measurement x

IMM States

IMM structure

Bayes teori

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

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

LPQ association: Plot & Track clusters

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.

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

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

Decentralized Multi-Radar Tracking

Centralized Multi-Radar Tracking

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

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

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

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

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

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

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