  Light-Emitting Diodes (LEDs) based underwater optical wireless communications
(UOWCs), a technology with low latency and high data rates, have attracted
significant importance for underwater robots. However, maintaining a controlled
line of sight link between transmitter and receiver is challenging due to the
constant movement of the underlying optical platform caused by the dynamic
uncertainties of the LED model and vibration effects. Additionally, the
alignment angle required for tracking is not directly measured and has to be
estimated. Besides, the light scattering propagates beam pulse in water
temporally, resulting in time-varying underwater optical links with
interference. We address the state estimation problem by designing an LED
communication system that provides the angular position and velocity
information to overcome the challenges. In this way, we leverage the power of
deep learning-based observer design to explore the LED communication's state
space properly. Simulation results are presented to illustrate the performance
of the data-driven LED state estimation.
