Information fusion

INFORMATION FUSION

11/19/2014INFORMATION FUSION 1

VII SEM INFORMATION TECHNOLOGY

NIT RAIPURSubmitted byParesh Sao (11118049)Tarun Behra (11118079)Vinit Payal (11118085)Anurag Pandey (11118013)Shivani Singh (11118068)

WHAT IS FUSION ?

Fusion Is the process of the joining two or more things together to form a single entity .

Two Types of fusions in the field of computer science:-

1. Data Fusion

2. Information Fusion

Data Fusion:-

Data fusion is used for raw data (obtained directly from the sensors)

Information Fusion:-

Information fusion is employed to define already processed data.

Information Fusion is the act or process of combining or associating dataor information from one or more sources to obtain improved informationfor detection, identification, or characterization of that entity.

INFORMATION FUSION:-

User node

Communication

Sourcenode

Data from Source

Sourcenode

Fuseddata

Source node

CLASSIFICATION OF DATA FUSION TECHNIQUES

1. Classification Based on the Relations between the Data Sources:-a) Complementary:-

when the information provided by the input sources representsdifferent parts of the scene and could thus be used to obtain more complete globalinformation. For example, in the case of visual sensor networks, the information on thesame target provided by two cameras with different fields of view is consideredcomplementary.

b) Redundant:-

when two or more input sources provide information about the sametarget and could thus be fused to increment the confidence. For example, the datacoming from overlapped areas in visual sensor networks are considered redundant.

A. data in-data out (DAI-DAO)

B. data in-feature out (DAI-FEO)

C. feature in-feature out (FEI-FEO)

D. feature in-decision out (FEI-DEO)

E. Decision In-Decision Out (DEI-DEO)

2. Dasarathy’s Classification:-

• A. Data in-data out (DAI-DAO):-

This type of data fusion process inputs and outputs raw data; theresults are typically more reliable or accurate. Data fusion at this level is conductedimmediately after the data are gathered from the sensors. The algorithms employed at thislevel are based on signal and image processing algorithms .

• B. data in-feature out (DAI-FEO):-

At this level, the data fusion process employs raw data from thesources to extract features or characteristics that describe an entity in the environment .

• C. feature in-feature out (FEI-FEO):-

At this level, both the input and output of the data fusion process arefeatures. Thus, the data fusion process addresses a set of features with to improve, refine orobtain new features. This process is also known as feature fusion, symbolic fusion,information fusion or intermediate-level fusion

• D. feature in-decision out (FEI-DEO):-

This level obtains a set of features as input and provides a setof decisions as output. Most of the classification systems that perform a decision based on asensor’s inputs fall into this category of classification.

• E. Decision In-Decision Out (DEI-DEO):-

This type of classification is also known as decision fusion. Itfuses input decisions to obtain better or new decisions.


INFORMATION FUSION ARCHITECTURE

There are three types of Information fusion Architecture

•Centralized

•Decentralize

•Hierarchal


CENTRALIZED

• It is the Simplest of all the architecture.• In this a central processor fuses the reports collected by all othersensing nodes i.e. the data is collected from different nodes and acentralized node produces the final output.

Advantage:Erroneous report(s) can be easily detected.

Disadvantage:Inflexible to sensor changes and the workload is concentrated at asingle point.

Centralized Fusion

Arch. Type : Centralized

Processing Load : High

Bandwidth : Moderate

Timeliness : Poor

Centralized Fusion Node(F)

S

S

C

S

C

C

SS: Sensor/Data Source.

C: Consumer


DECENTRALIZED

• Data fusion occurs locally at each node on the basis of local observations and the information obtained from neighboring nodes.

• No central processor node.

Advantages:Scalable and tolerant to the addition or loss of sensing nodes or dynamicchanges in the network.

Decentralized Cooperative Fusion Architecture

Node 1

Node 3Node 2Sensor measurement

report

Arch. Type : Peer to Peer

Processing Load : Low

Bandwidth : Very high

Timeliness : Very good


HIERARCHICAL

•Nodes are partitioned into hierarchical levels.•The sensing nodes are at level 0 and the BS at the highest level.•Reports move from the lower levels to higher ones.

Advantage:Workload is balanced among nodes

Distributed Hierarchical Information Fusion Architecture

Arch. Type : Hierarchical

Processing Load : High at mid levels

Bandwidth : Moderate

Timeliness : Good

C

F

F F

S S S S

C


BENEFITS FROM INFORMATION FUSION SYSTEM

•Fusion process is necessary most of all to reduce (to filter) input

information through its integration (merging) and generalization.

•Fusion process is necessary to improve accuracy.

Fusion process is necessary to reduce uncertainty.

11/19/2014

INFORMATION FUSION

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INFORMATION FUSION

APPLICATION

MILITARY APPLICATION :

•Location and characterization of enemy units and weapons

•High level inferences about enemy situation

•Air to air or surface to air defense

•Ocean monitoring

•Battlefield intelligence

•Strategic warning

NON MILITARY APPLICATIONS:

•Condition based maintenance

•Detection of system faults

•Robotics

•Medical

•Environmental monitoring

•Location and identification

of natural phenomena

Coding Part

Node 1

Turtle_bot

Node 2

Navigation Ser.

Use datafor

robot navigation

Robotnavigation


Send /scan data

Send /cmd_vel_nev

Showing Result

Analysis and give Distance and Time Value

• Andersson, L. A. (2008). Multi-robot Information Fusion. Sweden: UniTryck, Link¨oping.

• Arwin Datumaya, W. S. (2008). Design and Implementation of Multi Agent-basedinformation Fusion System for Decision Making Support.

• Kiril Alexiev, I. N. (2006). Methods for Data and Information Fusion .

• Xiang Li, M. S. (2009). Autonomous Information Fusion for Robust Obstacle Localization on a Humanoid Robot. The Latin American Robotics Symposium .


References

Engineering

Information fusion