©2001 CRC Press LLC
determination of location than could be obtained by either of the two independent sensors. This results
in a reduced error region, as shown in the fused or combined location estimate. A similar effect may be
obtained in determining the identity of an object based on observations of an object’s attributes. For
example, there is evidence that bats identify their prey by a combination of factors, including size, texture
(based on acoustic signature), and kinematic behavior.
1.3 Military Applications
The Department of Defense (DoD) community focuses on problems involving the location, character-
ization, and identification of dynamic entities such as emitters, platforms, weapons, and military units.
These dynamic data are often termed an order-of-battle database or order-of-battle display (if superim-
posed on a map display). Beyond achieving an order-of-battle database, DoD users seek higher-level
inferences about the enemy situation (e.g., the relationships among entities and their relationships with
the environment and higher level enemy organizations). Examples of DoD-related applications include
ocean surveillance, air-to-air defense, battlefield intelligence, surveillance and target acquisition, and
strategic warning and defense. Each of these military applications involves a particular focus, a sensor
suite, a desired set of inferences, and a unique set of challenges, as shown in Table 1.1.
Ocean surveillance systems are designed to detect, track, and identify ocean-based targets and events.
Examples include antisubmarine warfare systems to support Navy tactical fleet operations and automated
systems to guide autonomous vehicles. Sensor suites can include radar, sonar, electronic intelligence
(ELINT), observation of communications traffic, infrared, and synthetic aperture radar (SAR) observa-
tions. The surveillance volume for ocean surveillance may encompass hundreds of nautical miles and
focus on air, surface, and subsurface targets. Multiple surveillance platforms can be involved and numer-
ous targets can be tracked. Challenges to ocean surveillance involve the large surveillance volume, the
combination of targets and sensors, and the complex signal propagation environment — especially for
underwater sonar sensing. An example of an ocean surveillance system is shown in Figure 1.2.
Air-to-air and surface-to-air defense systems have been developed by the military to detect, track, and
identify aircraft and anti-aircraft weapons and sensors. These defense systems use sensors such as radar,
passive electronic support measures (ESM), infrared identification-friend-foe (IFF) sensors, electro-optic
TABLE 1.1
Representative Data Fusion Applications for Defense Systems
Specific Applications
Inferences Sought by Data
Fusion Process
Primary Observable
Data
Surveillance
Vo lume
Sensor
Platforms
Ocean surveillance Detection, tracking,
identification of targets
and events
EM signals
Acoustic signals
Nuclear-related
Derived observations
Hundreds of
nautical miles
Air/surface/sub-
surface
Ships
Aircraft
Submarines
Ground-based
Ocean-based
Air-to-air and surface-
to-air defense
Detection, tracking,
identification of aircraft
EM radiation Hundreds of miles
(strategic)
Miles (tactical)
Ground-based
Aircraft
Battlefield intelligence,
surveillance, and
target acquisition
Detection and
identification of potential
ground targets
EM radiation Tens of hundreds
of miles about a
battlefield
Ground-based
Aircraft
Strategic warning and
defense
Detection of indications of
impending strategic
actions
Detection and tracking of
ballistic missiles and
warheads
EM radiation
Nuclear-related
Global Satellites
Aircraft
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