Multi-Sensor Fusion Estimation Using Adaptive Zonotopic Kalman Filters With Binary Measurements

Abstract

This letter investigates the state estimation problem under binary sensors with inaccurate thresholds. A method for extracting zonotopic data from an inaccurate threshold model is proposed. The output of this method serves as measurements to construct a centralized zonotopic Kalman Filter (ZKF). By analyzing the characteristics of binary sensors, we further propose a threshold estimation technique to encapsulate the actual threshold within a zonotope. We demonstrate that the captured zonotope becomes increasingly tighter, thereby reducing the uncertainty of the threshold estimation. Additionally, through comparisons of estimation error bounds, we extend the proposed method to the sensor arrangement problem, addressing how to select the number of sensors and their thresholds. Circuit simulations validate the effectiveness of the proposed approach.