IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV - 91

DOI. No. 10.1109/MAES.2022.3166332
Abstracts of Previous Tutorials in This Series
TUTORIAL I
Published as Part 2 of IEEE Aerospace & Electronic
Systems Magazine
Volume Nineteen-Number One-January 2004
MULTIPLE HYPOTHESIS TRACKING FOR MULTIPLE TARGET
TRACKING
SAMUEL S. BLACKMAN
Raytheon Co., Segundo, CA
Multiple hypothesis tracking (MHT) is generally accepted
as the preferred method for solving the data association
problem in modern multiple target tracking (MTT)
systems. This paper summarizes the motivations for MHT,
the basic principles behind MHT and the alternative implementations
in common use. It discusses the manner in
which the multiple data association hypotheses formed by
MHT can be combined with multiple filter models, such as
used by the interacting multiple model (IMM) method. An
overview of the studies that show the advantages of MHT
over the conventional single hypothesis approach is given.
Important current applications and areas of future research
and development for MHT are discussed.
A STAP OVERVIEW
WILLIAM L. MELVIN, Senior Member, IEEE
GTRI, Smyrna, GA
This tutorial provides a brief overview of space-time
adaptive processing (STAP) for radar applications.
We discuss space-time signal diversity and various forms
of the adaptive processor, including reduced-dimension
and reduced-rank STAP approaches. Additionally, we
describe the space-time properties of ground clutter and
noise-jamming, as well as essential STAP performance
metrics. We conclude this tutorial with an overview of
some current STAP topics: space-based radar, bistatic
STAP, knowledge-aided STAP, multi-channel synthetic
aperture radar and non-sidelooking array configurations.
CLASS-SPECIFIC CLASSIFIER: AVOIDING THE CURSE OF
DIMENSIONALITY
PAUL M. BAGGENSTOSS, Member, IEEE
Naval Undersea Warfare Center Newport, RI
This article describes a new probabilistic method called
the " class-specific method " (CSM). CSM has the potential
to avoid the " curse of dimensionality " which
MAY 2022
plagues most classifiers which attempt to determine
the decision boundaries in a highdimensional featue
space. In contrast, in CSM, it is possible to build classifiers
without a common feature space. Separate lowdimensional
features sets may be defined for each class,
while the decision functions are projected back to the
common raw data space. CSM effectively extends the
classical classification theory to handle multiple feature
spaces. It is completely general, and requires no simplifying
assumption such as Gaussianity or that data lies
in linear subspaces.
" STATISTICS 101 " FOR MULTISENSOR, MULTITARGET DATA
FUSION
RONALD P. S. MAHLER
Lockheed Martin, Eagan, MN
This tutorial summarizes the motivations, concepts, techniques,
and applications of finite-set statistics (FISST),
a system-level, " top-down " direct generalization of ordinary
single-sensor, single-target engineering statistics to
the multisensor, multitarget realm. FISST provides powerful
new conceptual and computational methods for dealing
with multisensor, multitarget, and multi-evidence data
fusion problems. The paper begins with a broad-brush
overview of the basic concepts of FISST. It describes how
conventional single-sensor, single-target formal Bayesian
modeling is carefully extended to general data fusion
problems. We examine a simple example: joint detection
and tracking ofa possibly non-existent maneuvering target
in heavy clutter. The tutorial concludes with a commentary
on certain criticisms ofFISST.
TUTORIAL II
Published as Part 2 of IEEE Aerospace & Electronic Systems
Magazine
Volume Twenty-Number Eight-August 2005
A REVIEW OF DISTRIBUTED POWER SYSTEMS PART I:
DC DISTRIBUTED POWER SYSTEM
SHIGUO LUO
ISSA BATARSEH, Senior Member, IEEE
University of Central
Florida
The present development state in dc distributed power systems
(DPS) is comprehensively reviewed in this tutorial.
IEEE A&E SYSTEMS MAGAZINE
91
IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV

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