Kalman Filtering With Unlimited Sensing
Hongwei Wang, Xi Feng Zheng, Hongbin Li · 2024
In this paper, we consider state estimation in the Kalman filtering framework with unlimited sensing measurements (USMs), which are obtained from sensors equipped with a self-reset analog-to-digital (SR-ADC). SR-ADC was recently introduced to deal with the saturation issue frequently encountered in a conventional ADC. To tackle the nonlinearity of the USM, we present a unique decomposition property of the USM. Leveraging this property and a multiple model adaptive estimation strategy, we propose a novel USF-based Kalman filtering (KF-USM) algorithm. Numerical results reveal that the proposed KF-USM filter is an effective alternative to the conventional ADC-based KF to deal with high dynamic range input signals, offering more accurate state estimation in the presence of saturation.