Course

Elective Streams Software Defined Vehicles

Unit 1

Introduction to data fusion process- Data fusion models- Configurations and architectures – Probabilistic Data Fusion-Maximum Likelihood- Bayesian- Maximum Entropy methods – Recursive Bayesian methods- Kalman filter theory- Kalman filter as a natural data-level fuser.

Unit 2

Data fusion Methods: Data fusion by nonlinear Kalman filtering- Information filtering-H∞ filtering- Multiple hypothesis filtering- Data fusion with missing measurements- Possibility theory and Dempster-Shafer Method- ANN based decision fusion.

Unit 3

Decision theory based fusion and Evaluation: Decision theory based fusion- Bayesian decision theory- Decision making with multiple information sources- Decision making based on voting- Performance- Evaluation of data fusion systems- Monte Carlo methods – JDL process-Review of algorithms used for object refinement- Situation refinement- Threat refinement and process refinement.

Course Objectives

1. To provide knowledge on the fundamental concepts of data fusion processes, including various models, configurations, and architectures used in integrating multiple data sources.
2. To familiarize probabilistic data fusion techniques such as Maximum Likelihood, Bayesian methods, Maximum Entropy methods, and Recursive Bayesian methods.
3. To provide knowledge in implementing and utilizing Kalman filtering theory for data fusion, including nonlinear Kalman filtering, information filtering, H∞ filtering, and multiple hypothesis filtering.
4. To familiarize advanced data fusion methods including handling missing measurements, utilizing possibility theory and Dempster-Shafer Method etc.

Course Outcomes

CO-PO Mapping

Skills acquired

Proficiency in integrating and analyzing multiple data sources through probabilistic methods, Kalman filtering, and advanced fusion techniques and algorithms.

Text Books / References

1. Martin Liggins II, David Hall, and James Llinas, “Multi-Sensor Data Fusion: Theory and Practice”, CRC Press. – 2nd Ed., 2022,
2. Hassen Fourati, “Advances in Multi-Sensor Data Fusion: Algorithms and Applications” CRC Press, 1st Ed., 2023.
3. B. Mitchell , “Multi-Sensor Data Fusion: An Introduction”, CRC Press, 2^nd Ed.,2020,
4. David Hall and James Llinas, “Multi-Sensor Data Fusion: A Review of the State-of-the-Art”, Springer, 2nd Ed. 2021.
5. David Hall, James Llinas, “Principles of Multi-Sensor Data Fusion” Wiley-IEEE Press 2^nd Ed., 2019.
6. Jitendra R Raol, “Data Fusion Mathematics: Theory and Practice”, CRC Press,