Sensor Management for
Tracking
This half-day
tutorial will be held on the morning of Monday, April 11. The complete schedule
can be found here.
Presenters
Prof. Venugopal V. Veeravalli,
University of Illinois at Urbana-Champaign
Dr. George Atia, University of Illinois at Urbana-Champaign
Abstract
Large sensor
networks collecting data in dynamic environments are typically composed of a
distributed collection of nodes with limited energy and processing
capabilities. Hence, it is imperative to efficiently manage the sensors'
resources to prolong the lifetime of such networks without sacrificing
performance. This tutorial seeks to address the general problem of joint sensor
resource management for tracking applications. The fundamental goal is to
dynamically manage and control the sensing assets and modalities in order to
efficiently track the objects' physical information states (e.g., target
locations, speeds, types, number) subject to constraints on various resources
such as energy, communication, and computation.
The tutorial
is structured into four major parts. In part I, we introduce the mathematical
models for object(s) dynamics, the sensing system (including a description of
the sensing modalities) and the observation models. Since sensor management for
tracking is essentially a problem of filtering with dynamic sequential decision
making under uncertainty, in part II of this tutorial we aim to provide an
overview of non-linear filtering and Markov Decision Processes with special
emphasis on models where the state is only partially observable to the
controllers, due to noise and model uncertainties, natural limitations of the
measurement devices, or incomplete data about the surroundings. In Part III, we
address various problems in sensor management, including the problems of sensor
scheduling, sensor sleep management, and multi-modal control. The goal is to
design efficient control policies that yield the best tradeoffs between
tracking performance and resource usage. Exact solutions are generally
intractable even for the simplest models due to the dimensionality of the information
and control spaces. Hence, in part IV of this tutorial, we discuss approximate
solution techniques to design control policies ranging from simple myopic
policies, through information theoretic approaches, to approximation techniques
which use special surrogates to the optimal value function such as the
observable-after-control and the point-based type policies.
Biographies
Venugopal V. Veeravalli received the Ph.D. degree in 1992
from the University of Illinois at Urbana-Champaign, the M.S. degree in 1987 from
Carnegie-Mellon University, Pittsburgh, PA, and the B. Tech. degree in 1985
from the Indian Institute of Technology, Bombay, (Silver Medal Honors), all in
Electrical Engineering. He joined the University of Illinois at
Urbana-Champaign in 2000, where he is currently Professor in the department of
Electrical and Computer Engineering, Research Professor in
the Coordinated
Science Laboratory, and Director of the Illinois Center for
Wireless Systems (ICWS). He served as a program director for
communications research at the U.S. National Science Foundation in Arlington,
VA from 2003 to 2005. He has previously
held academic positions at Harvard University, Rice University, and Cornell
University, and has been on sabbatical at MIT, IISc
Bangalore, and Qualcomm, Inc. His research interests include distributed sensor
systems and networks, wireless communications, detection and estimation theory,
and information theory. He is a Fellow
of the IEEE and was on the Board of Governors of the IEEE Information Theory
Society from 2004 to 2007. He was an
Associate Editor for Detection and Estimation for the IEEE Transactions on
Information Theory from 2000 to 2003, and an associate editor for the IEEE
Transactions on Wireless Communications from 1999 to 2000. Among the awards he
has received for research and teaching are the IEEE Browder J. Thompson Best
Paper Award, the National Science Foundation CAREER Award, and the Presidential
Early Career Award for Scientists and Engineers (PECASE). He is a distinguished
Lecturer for the IEEE Signal Processing Society for 2010-2011.
George Atia is currently a postdoctoral
researcher with the Coordinated Science Laboratory at the
University of Illinois at Urbana-Champaign. He received his Ph.D. degree in Electrical and
Computer Engineering from Boston University, Massachusetts, in 2009. He
received the B.Sc. and M.Sc. degrees, both in Electrical Engineering, from
Alexandria University, Egypt, in 2000 and 2003, respectively. He is the
recipient of many awards, including the outstanding graduate teaching fellow of
the year award in 2003-2004, the 2006 College of Engineering Dean's Award at
the Science and Engineering Research Symposium, and the best paper award at the
International Conference on Distributed Computing in Sensor Systems (DCOSS) in
2008. His main research interests are in wireless communications, network
information theory, distributed systems, compressive sensing, and detection and
estimation theory.
Intended audience
The aim of
this tutorial is to present to a broad audience the mathematical foundations,
tools and algorithms for sensor management for tracking applications. The
intended audience includes graduate students, academic researchers as well as
industry participants who are interested in the general areas of tracking and stochastic
control.
Tutorial Structure
Part I:
Mathematical Models
·
Object
dynamics
·
Sensing
modalities
·
Observation
models
Part II:
Necessary background
·
Overview
of non-linear filtering theory
·
Markov
decision processes and POMDPs
Part III: Problems
·
Sensor
scheduling
·
Sensor
sleep management
·
Multi-modal
control
Part IV: Solution techniques