Information fusion

04/18/2023 INFORMATION FUSION 1

Submitted By :PARESH SAO(11118049)SHIVANI SINGH(11118068)TARUN BEHRA(11118079)VINIT PAYAL(11118085)

INFORMATION FUSION AND MAP BUILDING IN DISTRIBUTED SYSTEMS

VII SEMINFORMATION TECHNOLOGYNIT RAIPUR

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 implies a higher semantic level than data fusion.

Data or information to be fused can be from two sources:-

1. Sensors

2. Databases The goal of using data/Information fusion in multi sensor environments is to obtain a lower detection error probability and a higher reliability by using data from multiple sources.

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

Information Fusion:-

User node

CommunicationCommunication

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 represents different parts of the scene and could thus be used to obtain more complete global information. For example, in the case of visual sensor networks, the information on the same target provided by two cameras with different fields of view is considered complementary.

b) Redundant:-

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

c) cooperative:- when the provided information is combined into new information that is typically more complex than the original information. For example, multi-modal (audio and video) data fusion is considered cooperative.

2. Classification Based on the Abstraction Levels:-A. low level fusionB. medium level fusion C. high level fusionD. multiple level fusion

A. low level fusion:- The raw data are directly provided as an input to the data fusion process, which provide more accurate data (a lower signal-to-noise ratio) than the individual sources;

B. Medium level:-

Characteristics or features (shape, texture, and position) are fused to obtain features that could be employed for other tasks. This level is also known as the feature or characteristic level

C. High Level:-

This level, which is also known as decision fusion, takes symbolic representations as sources and combines them to obtain a more accurate decision. Bayesian’s methods are typically employed at this level

D. Multiple level fusion:

This level addresses data provided from different levels of abstraction (i.e., when a measurement is combined with a feature to obtain a decision) :-

3. Dasarathy’s Classification:-

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)

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

This type of data fusion process inputs and outputs raw data; the results are typically more reliable or accurate. Data fusion at this level is conducted immediately after the data are gathered from the sensors. The algorithms employed at this level 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 the sources 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 are features. Thus, the data fusion process addresses a set of features with to improve, refine or obtain 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 set of decisions as output. Most of the classification systems that perform a decision based on a sensor’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. It fuses 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 other sensing

nodes i.e. the data is collected from different nodes and a centralized node produces the final output.

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

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


CENTRALIZED FUSION

Arch. Type : Centralized Processing : Moderate Bandwidth : Moderate Timeliness : Poor Central

node

Sensor measurement

data


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 dynamic changes in the network.


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


HIERARCHICAL INFORMATION FUSION ARCHITECTURE

Arch. Type : Hierarchical Processing : High at mid levels Bandwidth : Moderate Timeliness : Good

Fusion Node

Sensor data 1

Sensor data 2

Sensor data 3

Sensor data 1+2

Sensor data 1+3


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.


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 1Turtle_bot

Node 2Navigation Ser.

Use data for

robot navigation

Robot

navigation

Send /scan data

Send /cmd_vel_nev Map

Generation

Processed /cmd_vel_nevWithSLAM


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

Arwin Datumaya, W. S. (2008). Design and Implementation of Multi Agent-based information 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 .

Engineering

Information fusion