Derivation of the sliding innovation information filter for target tracking

Abstract

An information filter is one that propagates the inverse of the state error covariance, which is used in the state and parameter estimation process. The term ‘information’ is based on the Cramer-Rao lower bound (CRLB), which states that the mean square error of an estimator cannot be smaller than an amount based on its corresponding likelihood function. The most common information filter (IF) is derived based on the inverse of the Kalman filter (KF) covariance. This paper introduces preliminary work completed on developing the information form of the sliding innovation filter. The SIF is a relatively new type of predictor-corrector estimator based on sliding mode concepts. In this brief paper, the recursive equations used in the sliding innovation information filter (SIIF) are derived and summarized. Preliminary results of application to a target tracking problem are also studied.