YUNMIN ZHU In the past two decades, multi sensor or multi-source
information fusion tech- niques have attracted more and more attention
in practice, where observations are processed in a distributed manner
and decisions or estimates are made at the individual processors, and
processed data (or compressed observations) are then transmitted to a
fusion center where the final global decision or estimate is made. A
system with multiple distributed sensors has many advantages over one
with a single sensor. These include an increase in the capability,
reliability, robustness and survivability of the system. Distributed
decision or estimation fusion prob- lems for cases with statistically
independent observations or observation noises have received significant
attention (see Varshney's book Distributed Detec- tion and Data Fusion,
New York: Springer-Verlag, 1997, Bar-Shalom's book
Multitarget-Multisensor Tracking: Advanced Applications, vol. 1-3,
Artech House, 1990, 1992,2000). Problems with statistically dependent
observations or observation noises are more difficult and have received
much less study. In practice, however, one often sees decision or
estimation fusion problems with statistically dependent observations or
observation noises. For instance, when several sensors are used to
detect a random signal in the presence of observation noise, the sensor
observations could not be statistically independent when the signal is
present. This book provides a more complete treatment of the
fundamentals of multi- sensor decision and estimation fusion in order to
deal with general random ob- servations or observation noises that are
correlated across the sensors.
