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 Filtering and Prediction Gating Track Initiation. Confirmation and Deletion Data Association (Correlation) Target dynamic and measurement model : Prediction model :

4. A Possible Situation ● ●z2 ● ●z1  z3 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 P D 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. Reaction Time Track Quality –Track Estimation State Estimation Error Radial Miss Distance –Track Purity (Misassociation) – the percentage of correctly associated measurements Measures of Performance (MOPs)