Sensor Fusion Application Case Study

Produce 10-15 minutes recorded presentation on any application of Sensor Fusion you find interesting. You can choose any application from any area of the syllabus (Sensor Networks, Kalman Filter, EKF, UKF, Particle Filter etc.) or related area (e.g. Kalman Filter Banks, Kalman Smoothing, Ensemble Kalman Filter, SLAM etc…). You may choose an autonomous vehicle application but alternatively feel free to choose any other application from whatever field you like. Feel free to email me suggestions for feedback if you wish.
You are welcome to work from a published source and use existing code, the main aim is to understand the application, the sensor fusion methods applied and how to implement them and understand, how the outcome is evaluated. Your presentation will be judged on:

• Quality of the presentation – in terms of interest/engagement/clarity/structure (50%)
• Understanding of and ability to explain/illustrate technical details (50%)
  Please refer to the marking rubric on the Moodle page.
  The presentation should be submitted by Monday 1st April. The video should be uploaded to the Assignment folder (see link in ‘Assignment 2’ Area of Moodle page).
  Example Topics:
• Time varying Kalman Filter – e.g. based on this MATLAB example: https://uk.mathworks.com/help/control/ug/state-estimation-using-time-varying-kalman-filter.html;jsessionid=7c8a79a7c9b4a854cf60819c3ee0
• Localisation with a Particle Filter – e.g. based on this Python example: https://ros-developer.com/2019/04/10/parcticle-filter-explained-with-python-code-from-scratch/
• Extended Object Tracking of Highway Vehicles with Radar and Camera https://uk.mathworks.com/help/driving/ug/extended-object-tracking.html (Goes beyond module syllabus)
  Please ensure you correctly reference any source material you use. An excellent assignment would extend beyond the source material.