ABSTRACT
Sensor fusion is a technique that combines data from multiple sensors such that the resulting information is more accurate and more reliable than that from a single sensor. Sensor fusion techniques are widely used in many applications such as mobile robot navigation, surveillance, air traffic control, and intelligent vehicle operations. This chapter presents the derivation of the square-root cubature information filter (SCIF) algorithm for multisensor fusion in a nonlinear Gaussian environment. It reviews information filtering in general. The chapter then derives the SCIF using the linear fusion theory and matrix algebra. The information filter is a modified version of the Kalman filter. The state estimates and their corresponding covariances in the Kalman filter are replaced by the information vectors and information matrices, respectively, in the information filter. The chapter considers the speed and rotor position estimation of a two-phase permanent magnet synchronous motor (PMSM).
