Term: Fall 2022 - Full Term (08/29/2022 - 12/12/2022)
Grade Mode: Letter Grading
|Start Date||End Date||Days||Time||Location|
|8/29/2022||12/12/2022||TR||11:10am - 12:30pm||KING N204|
[Topics for Fall 2022]
Multi-Sensor Data Fusion for Internet of Things Applications
Diliang Chen, Ph.D.
Office: Kingsbury W207
Office hour: Scheduled on request. Please send me an email to schedule office hour.
Multi-sensor data fusion is a technique of combining and integrating of data from multiple sensors to provide a more accurate and reliable view of data. This course aims to provide both insight understanding of theories and hands-on experiences related to multi-sensor data fusion. Lectures will provide a comprehensive introduction to effective sensor and data fusion methods that improve the probability of object tracking, target detection, classification, and identification. Hands-on projects will enhance the understanding and the application of each data fusion methods.
- Introduction on estimation theories and sensor fusion
- Sensor and data fusion architectures
- Kalman Filter (Linear, Extended, Unscented)
- Bayesian Networks
- Particle filter
- Convolutional Neural Networks
After taking this course, you will be able to:
- Understand the advantages of multi-sensor data fusion for object tracking and state estimation
- Understand the architectures for sensor fusion
- Be familiar with the processes of Multi-sensor data fusion
- Identify suitable multi-sensor data fusion algorithms for practical applications
- Implement different multi-sensor data fusion algorithms with MATLAB