Work

Extended Kalman filter demonstrated with a reconfigurable analog photonic neural network

Year: 2025

Type: article

Abstract: We demonstrate a reconfigurable fixed-point photonic neural network (PNN) based on a coherent photonic processor that interfaces with a digital architecture to perform motion tracking on a damped oscillator using the extended Kalman filter (EKF). The PNN performs comparably to a digital floating-point neural network in two demonstrations; motion tracking of a damped oscillator and voltage monitoring of an RC circuit with nonlinear capacitance. Though the photonic implementation introduces quantization errors, the results of two models over ∼500 time steps do not accumulate tracking errors, improving the uncertainty of the noisy observations by more than 4x in both applications.

Source: Optics Express

DOI: 10.1364/oe.568704

Authors: Andrew B. Klein, Zheyuan Zhu, Steven Silverio, Guifang Li, Shuo Pang

Language: English

Topic: Neural Networks and Reservoir Computing

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,270

Funders: Office of Naval Research, Army Research Office

Awards: G280113768, G3016666713