Milestone 1: Functional Platform & First Data Collection

• Make the mechanical assembly work, with two rotational degrees of freedom.

• Write or adapt the driver for the PNI RM3100 magnetometer.

• Develop the acquisition code and start collecting a first dataset.

• Tune the Kalman filter on PC (using MATLAB) with the experimental data.

• Implement the Kalman filter (as defined on PC) on the ESP32.

• Set up the display using the LVGL library.

• Test the device outdoors!
  (First milestone: have my own ultra-precise magnetometer.)

Milestone 2: Deep Learning & Real-Time Detection

• Collect data over several months.

• Train a neural network to correlate, for example, the “K factor” and my experimental measurements.

• Deploy the trained network on the ESP32 for real-time inference.
  (Second milestone: personal solar storm detector!)

Milestone 3: Power Optimization & Field Robustness

• Power management: implement sleep cycles on the ESP32.

• Reduce the number of acquisition cycles with movement, without degrading measurement quality.