Transcript Document

Multi-Sensor Data Fusion
H.B. Mitchell
UNCLASSIFIED
Multi-Sensor Data Fusion
Three-hour tutorial on multi-sensor
data fusion
The tutorial is closely based on a
selection of material taken from
the book:
Multi-Sensor Data Fusion: An
Introduction
by H.B. Mitchell
published by Springer-Verlag (2007)
1. Introduction
2. Sensors
3. Common Representational
Format
4. Spatial, Temporal, Semantic
Alignment
5. Robust Statistics
6. Ensemble Learning
UNCLASSIFIED
Introduction
UNCLASSIFIED
Multi-Sensor Data Fusion
Data fusion: Theory and Techniques which combine sensor data into a
common representational format. Aim is to improve the quality of
information.
Data fusion is analogous to the manner in which humans and animals
improve their chances of survival by exploiting their
Multiple senses
Experience
Ability to reason
Man-Made Fusion System
EO, IR, Radar
A priori information and/or historical
information
Bayesian inference, fuzzy logic,
Dempster-Shaefer
UNCLASSIFIED
Multi-Disciplinary Subject
Multi-sensor data fusion brings together many different techniques and
applications
Techniques
Bayesian networks
Signal Processing
Statistical Estimation
Tracking Algorithms
Classification Algorithms
Invariant Subspaces
MCMC
Genetic Algorithms
Bagging, Boosting
Computing Power
Applications
Fusion
UNCLASSIFIED
Medical Imaging
Remote Sensing
Surveillance
Data Mining
Computer Vision
Stereo Imaging
Complementary Fusion
After. Toet. Natural color mapping for multiand nightvision imagery. Information
Fusion (2003)
UNCLASSIFIED
Pan Sharpening
Panochromatic
image
Multi-spectral
image
UNCLASSIFIED
Pan-sharpened
image
Colorization
After. Toet. Natural color mapping for multiband nightvision
iamgery. Information Fusion (2003)
UNCLASSIFIED
Sensors
UNCLASSIFIED
Sensors
Sensors are devices which interact directly with environment
Sensors are the source of all input data. Often
use smart sensors which
•
•
•
Transform sensor signal to
standardized digital format
Calibrates sensor signal
Transmits sensor signal via
standardized interfaces.
sensor
element
Amp
UNCLASSIFIED
Filter
transmitter/
receiver
A/D
m
Fusion Node
Build a Data Fusion System as a distributed assembly of fusion nodes
Input
Data
Fusion
Output
Data
Aux Inform. Ext. Inform
UNCLASSIFIED
Logical Sensor
Logical sensor is any device which functions as a source of inform.
for a multi-sensor data fusion node
S1
S2
F1
F2
S1: Physical sensor
F1: Virtual Sensor: A fusion node
whose output is fed into
another fusion node
“virtual”
sensor
S3
UNCLASSIFIED
Sensor Errors
Sensors only give an estimate of the measured physical property
Nature of errors often determine the preferred fusion algorithm
Bias. Separately track with each
set of measurements, then fuse
tracks.
No Bias. Concatenate
measurements into one
vector then track with
Kalman Filter
UNCLASSIFIED
Common Representational Format
UNCLASSIFIED