Presented By :

Reg No: Branch: AEIE

7th Semester,2008Purushottam Institute of Engg

and Tech

The operation of multisensor fusion is a process of combining data that is obtained from homogeneous or heterogeneous sensors for certain sensing tasks to gain a broader/deeper and more accurate understanding of the entities of interest than that of uni-sensor sensing.

Example sensing/ tasks include Object/people tracking (3D position and

orientation) SMALLab, mixed reality rehab.,

Gesture recognition Gesture communication

…

Human Brain

Integrates sensory information to make inferences regarding the surrounding environment.

• Improved system performance – Improved detection, tracking, and

identification – Improved situation assessment and

awareness • Improved robustness – Sensor redundancy – Graceful degradation • Extended spatial and temporal coverage • Shorter response time • Reduced communication and computing

Detection, location , tracking and identification of military entities.

Sensors: radar, sonar, infrared, synthetic aperture radar (SAR), electro-optic imaging sensors etc.

Complex problem Large number and types of sensors

and targets ƒ Size of the surveillance volume

Real-time operational requirementsSignal propagation difficulties

Air traffic control Law enforcement Homeland security Medical diagnosis Robotics

ManufacturingƒHazardous workplace

Remote sensingƒCropsƒWeather patternsƒEnvironmentƒMineral resourcesƒBuried hazardous waste

Fusion across sensors. a number of sensors nominally measure the same

property, as, for example, a number of temperature sensors measuring the temperature of an object.

Fusion across attributes. a number of sensors measure different quantities

associated with the same experimental situation, as, for example, visual, pressure, sound

Fusion across domains. a number of sensors measure the same attribute over

a number of different ranges or domains, e.g., visual sensing using normal video cameras and IR cameras.

Fusion across time. current measurements are fused with historical

information, for example, from an earlier calibration. Often the current information is not sufficient to determine the system accurately and historical information has to be incorporated to determine the system accurately, for example, in video tracking.

Direct fusion of sensor data

Representation of sensor data via feature vector, with subsequent fusion of feature vector feature i.e.selection/reduction needs to be done to take

care of the curse-of-dimensionality problem.

Processing of each sensor to achieve high-level inferences or decisions, with subsequent

combination of the high-level results

Wir

ele

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han

nel

Without Fusion (Direct )

After Fusion

� Hand-held units

● Wand-mounted Sensors

• Wide band ground

penetrating radar

(GPR)

• Metal detector (MD)

Video SurveillanceTrackingActivity RecognitionMulti-modal Fusion

• Fusion for Detection/Classification/Tracking

Wireless Sensor Networks Vehicle Health Management • Environmental Quality Systems

Multisensor fusion and integration is a technique to get a better outpt or result by combining the outputs of more than one Sensors. It has a very wide range of applications and hence a require more development in this field.

G.L. Foresti, C.S. Regazzoni and P.K. Varshney (Eds.), Multisensor

Surveillance Systems : The Fusion Perspective , Kluwer Academic Press, 2003

Center of Excellence in Environmental and Energy Systems (http://eqs.syr.edu/)

Multisensor Data Fusion and Applications Pramod K. Varshney (Department of

Electrical Engineering and Computer Science,Syracuse University)

Questions ???