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304-649 Course Project Intro

304-649 Course Project Intro. IMM-JPDAF Multiple-Target Tracking Algorithm: Description and Performance Testing By Melita Tasic 3/5/2001. Overview. Multiple-targets in clutter; tracking principles and techniques Data Association Filtering and Prediction IMM-JPDAF

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304-649 Course Project Intro

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  1. 304-649 Course Project Intro IMM-JPDAF Multiple-Target Tracking Algorithm: Description and Performance Testing By Melita Tasic 3/5/2001

  2. Overview • Multiple-targets in clutter; tracking principles and techniques • Data Association • Filtering and Prediction • IMM-JPDAF • Measures of Performance

  3. Multiple -Target Tracking System Sensor data processing and measurement formation Data Association (Correlation) Track Initiation. Confirmation and Deletion Gating Filtering and Prediction Target dynamic and measurement model: Prediction model:

  4. ●z2 ● z3 ● ●z1 A Possible Situation Two targets in the same neighborhood as well as clutter.

  5. Data Association • Measurement–to-Track correlation-the key element of MTT • Deterministic (non-Bayesian) approaches • Probabilistic (Bayesian) approaches • Includes Gating • To decide if a measurement belongs to a established track or to a new target • Miscorrelation • Large prediction errors - tracks become ”starved” for observations, thus deleted • Unstable tracking decreased by increasing PD or by improved data association methods

  6. Filtering and Prediction • Incorporates correlating observations into the update track estimates • Typical choice - Kalman filter • Advantages • associated covariance matrix can be used for gating • Provides convenient way to determine filter gains as a function of assumed measurement model, target maneuver model and measurement sequence • Cost • Additional computations and storage requirements

  7. IMM-JPDAF • IMM - Interactive multiple model approach • Obeys one of finite number of r of motion models (modes) • The filter switches between modes according to a Markov chain • JPDAF - Joint Probability Data Association Filter • Multi-hypotheses are formed after each scan, but combined before the next scan of data is processed • Used for calculations of association probabilities, using all measurements and all tracks • Association probabilities used for the track update

  8. Measures of Performance (MOPs) • Reaction Time • Track Quality • Track Estimation • State Estimation Error • Radial Miss Distance • Track Purity (Misassociation) – the percentage of correctly associated measurements

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