Modifying the Kalman Filter for Random Jitter in Sampling Time

Abstract

It is known that time jitter can vary in nature and magnitude depending on how accurately the time scale is generated and the dynamic process is sampled. We modify the Kalman filter for white Gaussian random jitter and call it jitter Kalman filter (JKF). It is shown that to cope with time jitter the system noise covariance acquires an additional term proportional to the fractional time jitter standard deviation and the process rate. Based on numerical simulations, it is shown that if the process rate grows without limits then the estimation error caused by time jitter will also grow without limits. The conclusions are confirmed experimentally