Data-driven state estimation for light-emitting diode underwater optical communication?

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 nonlinearities and time-varying underwater optical links with interference and introducing challenges in the estimation problem. In this paper, we address the state estimation problem by designing a Luenberger observer for the LED communication system that provides the angular position and velocity state information involved in the challenges of maintaining a controlled LOS optical wireless communication. In this line, we leverage the power of deep learning-based observer design to estimate the state of the LED communication model online. Simulation results are presented to illustrate the performance of the data-driven LED state estimation.