Elective Streams Software Defined Vehicles
Course Name | Multi Sensor Data Fusion |
Course Code | 24AT736 |
Program | M. Tech. in Automotive Engineering |
Credits | 3 |
Campus | Coimbatore , Chennai , Bengaluru , Amritapuri , Kochi |
Elective Streams Software Defined Vehicles
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.
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.
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
Course Outcomes
CO |
CO Description |
CO1 |
Design data fusion systems using various probabilistic methods, including Maximum Likelihood estimation, Bayesian inference, and Maximum Entropy methods. |
CO2 |
Apply Kalman filtering theory for data fusion tasks, including understanding its theoretical foundations, implementing nonlinear Kalman filtering techniques, and utilizing information filtering and multiple hypothesis filtering approaches. |
CO3 |
Handle complex data fusion scenarios such as missing measurements, utilizing possibility theory and Dempster-Shafer Method for uncertainty management, and employing ANN-based decision fusion techniques. |
CO4 |
Evaluate the performance of data fusion systems using Monte Carlo methods, understanding the Joint Directors of Laboratories (JDL) process, and reviewing algorithms for object refinement |
CO-PO Mapping
PO1 |
PO2 |
PO3 |
PO4 |
PO5 |
|
CO1 |
3 |
1 |
1 |
3 |
|
CO2 |
2 |
1 |
1 |
3 |
|
CO3 |
3 |
2 |
1 |
1 |
3 |
CO4 |
3 |
2 |
1 |
2 |
3 |
Skills acquired
Proficiency in integrating and analyzing multiple data sources through probabilistic methods, Kalman filtering, and advanced fusion techniques and algorithms.
Text Books / References
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