ISIS project: Sensor Fusion
This project is carried out by
Division of Automatic
Control and Communication Systems in cooperation with
SAAB Aerospace,
SAAB Bofors Dynamics
and
NIRA Dynamics.
Selected publications
F. Gustafsson, F. Gunnarsson, N. Bergman, U. Forssell, J. Jansson, R. Karlsson,
and PJ. Nordlund.
Particle filters for positioning, navigation and tracking.
IEEE Transactions on Signal
Processing, Vol. 50, Nr. 2, 2002.
pdffile.
N. Persson, F. Gustafsson and M. Drevö.
Indirect tire pressure monitoring using sensor fusion.
In SAE 2002, Detroit, 2002. Report number 2002011250.
pdffile.
PJ. Nordlund and F. Gustafsson.
Recursive estimation of threedimensional aircraft position using
terrainaided positioning.
Proc. of ICASSP, Orlando, May, 2002.
pdffile
PJ. Nordlund, F. Gunnarsson and F. Gustafsson.
Particle filters for positioning in wireless networks.
Proc. of EUSIPCO (invited paper), Toulouse, France, September, 2002.
pdffile.
Urban Forssell, Peter Hall, Stefan Ahlqvist, Fredrik Gustafsson.
Novel MapAided Positioning System.
Proc. of FISITA, Helsinki, Finland, 2002.
pdffile.
Positioning using timedifference of arrival measurements.
Fredrik Gustafsson and Fredrik Gunnarsson.
ICASSP, Hongkong, PRC, 2003.
pdffile or
psfile.
Particle filter and CramerRao lower bound for underwater navigation.
Rickard Karlsson, Fredrik Gustafsson and Tobias Karlsson.
ICASSP, Hongkong, PRC, 2003.
pdffile or
psfile.
Highlights 2002
Three licentiate theses were presented:

Rickard Karlsson.
Simulation Based Methods for Target Tracking.
LIUTEKLIC2002:930, Dept. of Electrical Engineering, Linköping
University, Sweden, 2002.

PerJohan Nordlund.
Sequential Monte Carlo filters and Integrated Navigation.
LIUTEKLIC2002:945, Dept. of Electrical Engineering, Linköping
University, Sweden, 2002.

Niclas Persson.
Event Based Sampling with Application to Spectral Estimation.
LIUTEKLIC2002:981, Dept. of Electrical Engineering, Linköping
University, Sweden, 2002.
A project survey paper appeared, describing most of the projects above and a
general framework for the particle filter.
F. Gustafsson, F. Gunnarsson, N. Bergman, U. Forssell, J. Jansson, R. Karlsson,
and PJ. Nordlund.
Particle filters for positioning, navigation and tracking.
IEEE Transactions on Signal Processing, 2002.
pdffile.
The author of the master thesis
Hall, Peter.
"A Bayesian approach to MapAided Vehicle Positioning".
NIRA Dynamics, LiTHISYEX3102, 2001.
received an award (nonexisting link removed).
The project was successfully continued in a student project and
another master thesis. The result is a complete navigator
including voice guidance implemented on a Compaq iPAQ handheld computer.
This is probably the world's first
demonstrator of a real application based on a particle filter.
A new company, NIRA Dynamics, entered ISIS and the sensor fusion project.
A workshop was organized on particle filters for navigation,
including a plenary talk and three master thesis presentations at our
three industrial partners. The workshop was attended by about 50 people.
Overview
The research in the area of sensor fusion
is focused on target tracking, navigation and positioning
applications.
The common denominator here is that these problems can be written as
nonlinear state space models with nonGaussian noise:
x(t+1) = f(x(t)) + w(t)
y(t)=h(x(t)) + e(t)
where the state vector contains position, velocity and other dynamical
states, and the measurements come form sensors as gyroscopes,
accelerometers, GPS, radar, etc.
The filtering problem is to estimate the states given the
measurements. The classical approach is to linearize the state space
model, to assume Gaussian noise and then applying the Kalman filter.
The particle filter provides a general algorithm for approximating
a posteriori distirbution of the states with arbitrary accuracy.
The framework is particular suitable for sensor fusion, where
sensor information of different kind is mixed with e.g.
information from digital maps.
We have the last few years developed efficient and robust particle
filter algorithms to approximate the a posteriori distribution
of the state vector, and applied the algorithms to a number of
applications:
 Terrain navigation.
In this application an aircraft position is
autonomously determined by fusing measurements from an inertial
navigation system, a digital map and a radar altimeter. By
measuring the terrain height variations along the aircraft
flightpath and comparing these with a digital terrain map, a
position estimate of the aircraft is obtained. The comparison
between the map and the measurements is a nonlinear estimation
problem where unconventional and conceptually different sources of
information are fused together. Research as been focused on
finding a reliable and effective algorithm for this position
determination. See the paper
N. Bergman, L. Ljung, and F. Gustafsson.
Terrain navigation using Bayesian statistics.
IEEE Control System Magazine, 19(3):33  40, 1999.
pdffile.
 Underwater navigation. The terrain navigation project has been
extended to underwater navigation using sonar depth
measurements. An underwater terrain map replaces the terrain
map above, and similar inertial sensors can be used.
The CramerRao lower bound on the estimation accuracy has been
derived, and a simple interpretation is that
Position accuracy = square root( velocity sensor variance * sonar
sensor variance / terrain variation)
See the image above,
particle filter master theses overview and the conference paper
Rickard Karlsson, Fredrik Gustafsson and Tobias Karlsson.
Particle filtering and CramerRao lower bound for underwater naviation.
ICASSP, Hongkong, PRC, 2003.
pdffile or
psfile.
 Map aided positioning for vehicles.
An approach similar to terrain navigation can be applied to cars, when
a street map is available. The velocity of the wheels is
compared to the street map, and after a few turns the correct
position is usually found.
See the
particle filter master theses overview, the
awarded master thesis summary (nonexisting link removed)
and the conference paper
Urban Forssell, Peter Hall, Stefan Ahlqvist, Fredrik Gustafsson.
Novel MapAided Positioning System.
Proc. of FISITA, Helsinki, Finland, 2002.
pdffile.

Target tracking. Target tracking algorithms for torpedos hunting ships
using bearingsonly measurement and for aircraft hunting ships
using map information are presented in Rickard Karlsson's
licentiate thesis. Also the data association problem has been studied.
 Mobile terminal positioning. A survey of tracking algorithms for
mobile terminals is given in
PJ. Nordlund, F. Gunnarsson and F. Gustafsson.
Particle filters for positioning in wireless networks.
Proc. of EUSIPCO (invited paper), Toulouse, France, September, 2002.
pdffile.
Positioning using timedifference of arrival measurements based on
both a stochastic gradient algorithm and particle filter is
described in
Fredrik Gustafsson and Fredrik Gunnarsson.
Positioning using timedifference of arrival measurements.
ICASSP, Hongkong, PRC, 2003.
pdffile or
psfile.