State Estimation for a Carbon Nanotube-based Sensor Array System

Abstract

This paper proposes state estimation methods for tracking a time-varying local concentration of target molecules from the carbon-nanotube (CNT)-based sensing system. The signals triggered by adsorption/desorption events of a trace of the proximate target molecules on the sensors show strongly stochastic behavior. Various state estimation methods including the Kalman filter (KF), particle filter (PF), and moving horizon estimator (MHE) are designed for the system with highly stochastic, non-Gaussian measurements.