IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV - 95

IEEE AES Tutorials
framework and to focus more on deterministic and geometric
concepts. This work also takes into consideration
these latest trends, providing a wide theoretical review
without disregarding practical recommendations about
algorithm implementation. The main open research topics
are addressed as well, the foremost being algorithm optimization,
which is required for embodying anomaly detectors
in real-time systems.
ON BAYESIAN TRACKING AND DATA FUSION: A TUTORIAL
INTRODUCTION WITH EXAMPLES
WOLFGANG KOCH, Senior Member, IEEE
Fraunhofer-FKIE
Germany
This tutorial paper provides a short introduction to
selected aspects of sensor data fusion by discussing characteristic
examples. We consider three cases when fusion
of sensor data is important: when emphasis is placed on
data produced at different instants of time (i.e., target
tracking), when data being collected from different sensor
sources are important, and when we have data with background
information on the sensor performance as well as
data with nonsensor context information. The feedback
from data processing to the data acquisition process is
illustrated by a sensor management example.
PARTICLE FILTER THEORY AND PRACTICE WITH POSITIONING
APPLICATIONS
FREDRIK GUSTAFSSON, Senior Member, IEEE
Link€oping University
Sweden
The particle filter (PF) was introduced in 1993 as a numerical
approximation to the nonlinear Bayesian filtering
problem, and there is today a rather mature theory as well
as a number of successful applications described in literature.
This tutorial serves two purposes: to survey the part of
the theory that is most important for applications and to survey
a number of illustrative positioning applications from
which conclusions relevant for the theory can be drawn.
The theory part first surveys the nonlinear filtering problem
and then describes the general PF algorithm in relation to
classical solutions based on the extended Kalman filter
(EKF) and the point mass filter (PMF). Tuning options,
design alternatives, and user guidelines are described, and
potential computational bottlenecks are identified and
remedies suggested. Finally, the marginalized (or RaoBlackwellized)
PF is overviewed as a general framework
for applying the PF to complex systems.
The application part is more or less a stand-alone tutorial
without equations that does not require any background
knowledge in statistics or nonlinear filtering. It describes a
number of related positioning applications where
MAY 2022
geographical information systems provide a nonlinear
measurement and where it should be obvious that classical
approaches based on Kalman filters (KFs) would have
poor performance. All applications are based on real data
and several ofthem come from real-time implementations.
This part also provides complete code examples.
TUTORIAL VI
REVIEW OF RANGE-BASED POSITIONING ALGORITHMS
JUNLIN YAN
Intel Mobile Communications GmbH
CHRISTIAN C. J. M TIBERIUS & GERARD
J. M JANSSEN
Delft University ofTechnology
PETER J. G. TEUNISSEN
Curtin University ofTechnology
GIOVANNI BELLUSCI
Xsens
This tutorial reviews algorithms which turn measured
ranges into position solutions. From their basic mathematical
principles, we relate and compare relevant aspects of
these algorithms. Special attention is given to the direct
(non-iterative) algorithms, which are frequently applied in
indoor positioning. Most of them are shown to be essentially
the same, as they can be related through applying
different weighting schemes. This tutorial is intended as a
useful guide to help researchers and system designers
evaluate and select appropriate range-based positioning
algorithms for their applications at hand.
TUTORIAL VII
BASIC TRACKING USING NONLINEAR 3D MONOSTATIC AND
BISTATIC MEASUREMENTS
DAVID CROUSE
Naval Research Laboratory
Washington, DC USA
Monostatic and bistatic position and Doppler measurements
used in radar and sonar systems are nonlinear transformations
of a Cartesian state. These nonlinearities pose a challenge
for many target tracking algorithms, causing the socalled
contact lens problem, which describes the nonlinear
appearance ofthe measurement probability density function
in Cartesian coordinates. This tutorial considers methods for
measurement filtering (tracking without considering data
association) using a singleGaussian approximation when
monostatic and bistatic position and Doppler measurements
are available. The connection between the cubature Kalman
filter and numerous other filtering algorithms is shown, and
IEEE A&E SYSTEMS MAGAZINE
95
IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV

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