8:00  9:00 a.m. 
"Fundamentals
of Small Target Tracking"
T.
Kirubarajan
McMaster University
Hamilton, Ontario, CANADA
Target tracking consists of data
association, filtering and fusion, and a number of algorithms are
available to handle these issues in tracking small (or point) targets.
In this presentation, we will review different algorithms, including the
Kalman filter, Interacting Multiple Model (IMM) estimator, Probabilistic
Data Association (PDA) algorithm, Multiple Hypothesis Tracking (MHT)
algorithm and assignment techniques. Possible fusion architectures and
performance measures for target tracking will be discussed as well.
Simulation results on some realistic tracking scenarios will be
presented.

9:00  9:55 a.m. 
"Engineering
Statistics for MultiObject Tracking"
Ronald P. Mahler
Lockheed Martin Tactical
Systems
Eagan, MN, USA
Progress in
singlesensor, singletarget problems has been greatly aided by the
existence of a systematic, rigorous, and yet practical engineering
statistics. One might expect that the same would be true for
multisensormultitarget problems. Surprisingly, this has not been the
case, even though a comprehensive statistical foundation for multiobject
problems—point process theory—has been in existence for decades.
The primary purpose of this tutorial is to provide a brief, highlevel
overview of finiteset statistics (FISST), the ''engineering
friendly'' version of point process theory that Dr. Mahler introduced in
1994. FISST is engineeringfriendly in that it is geometric, and
preserves the “Statistics 101” formalism that signal processing engineers
already understand. Its core is a multisourcemultitarget differential
and integral calculus, based on the fact that beliefmass functions
are the rigorous multisensormultitarget counterparts of probabilitymass
functions. One novel consequence is that FISST encompasses expertsystem
approaches such as fuzzy logic, the DempsterShafer theory, and rulebased
inference. A secondary purpose of the tutorial is to demonstrate the
relevance of FISST to practical applications such as robust INTELL
multisource NCTI, multitarget tracking, and performance evaluation. A
third purpose is to address such few criticisms of FISST as there have
been. The optimality and simplicity of Bayesian methods can be taken for
granted only within the confines of standard applications addressed by
standard textbooks. This tutorial will show that when one ventures out of
these confines—especially in multitarget problems—complacency can lead to
serious problems. 
10:05  11:00 a.m 
"Sequential
Monte Carlo Methods for MultiObject Tracking"
Simon Maskell
QinetiQ
and Cambridge University
ENGLAND
Sequential Monte Carlo methods, or particle
filters, provide a powerful Bayesian methodology for sequential inference
in nonlinear nonGaussian statespace systems. After an introduction to
the approach, in an attempt to improve intuition as to how particle
filters can be used to track multiple targets, two thrusts of current
research will be described within the context of importance sampling.
Firstly, the crucial role of the choice of importance distribution will be
described in terms of changing the memory of a system that the samples
must populate. Secondly, the use of analytic integration to reduce the
MonteCarlo variance will be explained through consideration of the
resultant reduction in dimensionality of the statespace that the samples
inhabit. 
11:00  noon 
"Visual Tracking of Multiple Objects
Using Particle Filters"
John MacCormick
HP Labs
Palo Alto, CA, USA
After reviewing the fundamental theory of
multiobject particle filters, we'll describe the popular approaches to
using particle filters in visual tracking problems. In particular, we will
investigate the answers to the following two questions: (i) How can all
appropriate data from a video sequence be incorporated into the inference
process, so that particles hypothesizing different numbers of objects can
be treated coherently? (ii) How can object births and deaths be modeled
without either destroying the probabilistic validity of the particle
filter or creating an excessive computational burden? In addition, we will
discuss strategies for variance reduction that are particularly suited to
visual tracking problems. 