Speakers
IMU augmentation using Deep Learning
This talk discusses how low-cost MEMS based Inertial Measurement Units (IMUs) can be improved by applying ML techniques to reduce accelerometer and gyroscope errors.
Freddy is a Chartered Physicist, who graduated from the University of Exeter in 2018 with a first-class honours degree in Physics. During his studies, he received the Exeter Physics Award, the Dean’s Commendation, and completed a year‑long placement at the Home Office Centre of Applied Science and Technology. Freddy joined Plextek in 2018, undertaking low‑to-mid‑TRL research primarily for defence applications, including: applying Deep Learning methods to exploit micro‑Doppler signatures for UAV classification, demonstrated transfer‑learning approaches across different sensor modalities, and helped develop a machine‑learning‑based RF propagation model.