Transcript 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
• Measures of Performance
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 x ( k  1)  F ( k ) x ( k )  v ( k )
model:
z ( k )  H ( k ) x ( k )  w( k )
Prediction model:
ˆ (k  1 | k )  F (k ) x
ˆ (k | k )
x
ˆ (k  1 | k )
ˆ ( k  1 | k )  H ( k  1) x
z
A Possible Situation
Two targets in the same neighborhood as well as clutter.
zˆ1
z3
●
●z2
●
zˆ2
●z1
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
Measures of Performance
(MOPs)
• Reaction Time
• Track Quality
– Track Estimation
• State Estimation Error
• Radial Miss Distance
~x (k | n)  x(k )  xˆ(k | n)
RMD ( xˆ, x) | xˆ  x |
– Track Purity (Misassociation) – the
percentage of correctly associated
measurements