On Reducing Estimation Error Caused by Variable Sampling Rate

In this paper, we show that a variation in sampling rate give rise to system performance degradation and propose a method to effectively reduce the error. We first capture the variation as a first order autoregressive (AR) model and project it as an additional sensor measurement noise. By considering that the sensor measurements include correlated noise, we perform a decorrelation process and then apply a standard Kalman filter (SKF) to estimate the target-state. As a result of the two-step procedure, we achieve a significant reduction in the target state estimation error.