Multirate Interlaced Kalman Filter

Large systems are typically partitioned into many subsystems to reduce computational load. For this reason, the Interlaced Extended Kalman Filter (IEKF) was created, in which each subsystem estimates only its own state while utilizing information from other subsystems. The information shared is normally the a-priori and a-posteriori state, as well as the a-priori and a-posteriori covariance matrix. Subsystems, however, cannot, for technological reasons, always operate at the same rate. To address this issue, we propose a multirate distributed filter, in which the subsystems operate independently and only share information when a novel measurement activates each subsystem. The only information exchanged is the a-posteriori state and covariance matrix. In the paper, we demonstrate that the proposed filtering technique is accurate and effective by examining the convergence property. A water tank case study is detailed, and two subsystems with different but fixed rates are discussed, illustrating the efficiency of the proposed solution. The same approach can be modified to take into account numerous instances of subsystems as well as missing data due to an unreliable communication route.