304-649 course project intro imm-jpdaf multiple-target tracking algorithm: description and...
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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
• Measures of Performance
Multiple -Target Tracking System
Sensor data processing and measurement
formation
Filtering and Prediction
Gating
Track Initiation. Confirmation and Deletion
Data Association (Correlation)
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Target dynamic and measurement
model:
Prediction model:
A Possible Situation
● ●z2
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●z1
z3
2z
1z
Two targets in the same neighborhood as well as clutter.
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
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
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
• 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)
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