phd approach for multi-target tracking nikki hu nikki hu
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PHD Approach PHD Approach for Multi-target for Multi-target
TrackingTracking
Nikki HuNikki Hu
OutlineOutline
AcknowledgementAcknowledgement Review of PHD filterReview of PHD filter SimulationSimulation Further workFurther work
AcknowledgementsAcknowledgements
Much of this work is from Tracking Much of this work is from Tracking and Identifying of Multiple Targetsand Identifying of Multiple Targets
Code modified from Matlab codesCode modified from Matlab codes
Review of PHD FilterReview of PHD Filter
Multitarget Bayes FilterMultitarget Bayes Filter M.T. 1M.T. 1stst-Moment Filter-Moment Filter PHD Filter ImplementationPHD Filter Implementation
Particle-System Equations for PHD Particle-System Equations for PHD MassMass
Updates for ParticlesUpdates for Particles
Multitarget Bayes Multitarget Bayes FilterFilter
sensors
Zk+1targets
data
Zk = Tk Ck
multitarget motion
Tk+1= Tk Bk
multitarget Markov motionmodel
multitarget time prediction
fk+1|k(Y|Z(k)) = fk+1|k(Y|X) fk|k(X|Z(k))X
multisensor-multitargetBayes update
multisensor-multitarget
likelihoodfunction
fk+1|k+1(X|Z(k+1)) f(Zk+1|X) fk+1|k(X|Z(k))Xk+1^
multitarget state estimation
M.T. 1M.T. 1stst-Moment -Moment FilterFilter
fk|k(X|Z(k)) fk+1|k+1(X|Z(k+1)) fk+1|k(X|Z(k))
k|k k+1|k+1k+1|k
multitargetBayes filter
use filter that propagates multitarget first-moment densities
observation space
single-target state space
Time-update step
Data-update step
1st –moment (PHD)Fillter
)|( )(|
kkk ZXD )|( )(
|1k
kk ZXD )|( )1(1|1
kkk ZXD
time-updatestep
data-updatestep
Dk+1|k(x|Z(k)) Dk+1|k+1(x|Z(k+1))Dk|k(x|Z(k))1st-moment(PHD) filter
compress tofirst moment
compress tofirst moment
compress tofirst moment
PHD PHD ImplementationImplementation
Strong convergence propertiesStrong convergence properties for every observation sequence, particle for every observation sequence, particle distribution converges a.s. to posteriordistribution converges a.s. to posterior
computationally efficient ( computationally efficient ( OO((NN), ), NN = no. of particles) = no. of particles)
PHD, time k PHD, time k+1
“particles”= samples
Deltafunctions
propagation of particles
Sequential Monte-Carlo (Particle Filters)
Particle-System Particle-System Equations for PHD Equations for PHD
MassMass
N
1i
ikkk1kN
MN
1i
ikkk1kN
Mk1kk1k XBXqBM kkkk
||||||||
mean no. birthsprobability of survival mean no. of
offspring
N
i
ikkD
kk
ZzN
i
ikk
ikkD
kkkk
ikk
N
i
ikkD
kk
kk XpN
M
XzfXpN
Mzc
XzfXpN
M
Mk 1
|1|1
1|1|1
|111
|11
|1|1
1|1 1|
|
Time Update:
Observation Update:
observation likelihood
prob. detection
clutter densitymean no. false alarms
Monte Carlo samplesPHD mass
Motion UpdateMotion UpdateAssume no target spawning and Assume no target spawning and death probability is independent of death probability is independent of target state.target state.
Update particles using Markov Update particles using Markov density.density.
Resample particles Resample particles using spontaneous birth distributionusing spontaneous birth distribution
Updates for Updates for ParticlesParticles
Observation UpdateObservation Update Assume single sensor, and Assume single sensor, and ppDD is is
independent of independent of XX..
Compute a weight for each particle Compute a weight for each particle (using below) and resample particles (using below) and resample particles according to the induced distributionaccording to the induced distribution
Problem 1Problem 1
How to extract state from a PhD?How to extract state from a PhD? User wants to know target positions. User wants to know target positions. Does not want to see a Poisson process Does not want to see a Poisson process
density function.density function.
Are there efficient algorithms for Particle Are there efficient algorithms for Particle Filter implementation of PHD?Filter implementation of PHD?
Example 1Example 1
Current techniques Current techniques rely on peak and/or rely on peak and/or cluster detection cluster detection algorithms.algorithms.
2 dxZxD kk
Example 2Example 2
Peak detection Peak detection algorithms are not algorithms are not a universal a universal solution:solution: 2 dxZxD k
k
Two Targets Tracking
Three Targets Tracking
Graphs Get from Matlab Codes
System Mass
System Particles
System Targets
System Targets(.) and estimated System Targets(x)
Further workFurther work
Change Observation ModelChange Observation Model Change Interacting Particle Change Interacting Particle
implementation to SERPimplementation to SERP