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)

)|1(ˆ)1()|1(ˆ

)|(ˆ)()|1(ˆ

)()()()(

)()()()1(

kkxkHkkz

kkxkFkkx

kwkxkHkz

kvkxkFkx

Target dynamic and measurement

model:

Prediction model:

A Possible Situation

● ●z2

●

●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)

)|(ˆ)()|(~ nkxkxnkx

|ˆ|),ˆ( xxxxRMD