Comparison of Linear Filters in the Presence of Biased Measurements
Paul A. Miceli, William Dale Blair · 2019
In addition to the typical random errors that vary between consecutive measurements, the measurements for most all sensors used for target tracking include bias errors that remain fixed during a target tracking episode and are typically characterized by an a priori mean and covariance. Several extensions to the Kalman filter have been developed to consider the bias error statistics when calculating a state error covariance. In this paper, several of these extensions are compared by deriving analytical forms of the steady-state covariance with nearly fixed and nearly constant velocity targets in a scalar coordinate. A novel method for computing the measurement covariance inflation required to contain the bias and random errors at the output of the filter is presented and illustrated.