Accounting for simulation errors in continuous-discrete Kalman filtering

Abstract

In continuous-discrete Kalman filter implementations there is a tradeoff between the computational requirements for real time implementation, and errors incurred by selection of step size and method order in the simulation of the continuous time system model between sampling instances. Because the Kalman filter corrects errors in the state vector using output measurements, a systematic approach to account for the effects of simulation errors in the continuous-discrete Kalman filter allows the filter to be designed to deal with simulation errors in addition to conventional process (state) and measurement (output) noise.