Sensor Data Fusion

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Sensor Data Fusion Syllabus — Unit-wise Content

The official GTU curriculum for Sensor Data Fusion (subject code 2730306) as prescribed for IACI · Semester 3 divides the course into 5 units, covering 26 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.

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Unit 1 Introduction

Sensors and sensor data
Limitations of single sensor
Advantages of multisensor data fusion
Multisensor data fusion applications
Data fusion models
Generic fusion architectures 6 5-10%

Unit 2 Algorithms for Data Fusion

Taxonomy of algorithms for multi-sensor data fusion
Learning of fusion models: Learning Bayesian classifier
Rule learning from decision three algorithms. 6 5-15%

Unit 3 Estimation

Kalman filtering
practical aspects of Kalman filtering
extended Kalmal filters
partical filter
Decision level identify fusion
Knowledge based approaches. 10 15-25%

Unit 4 Advanced Filtering

Data information filter
extended information filter
Decentralized and scalable decentralized estimation
Sensor fusion and approximate agreement
Optimal sensor fusion using range trees recursively
Distributed dynamic sensor fusion. 10 20-30%

Unit 5 High Performance Data Structures

graphs and function
Representing ranges and uncertainty in data structures
Designing optimal sensor systems within dependability bounds
Implementing data fusion system
Application of multisensor data fusion for mobile robot mapping and Navigation. 10 15-25%

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Sensor Data Fusion

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