Sensor Fusion and State Estimation: From the Kalman Filter to Complementary Orientation Filters

Combining measurements from several sensors is a basic task in cyber-physical systems. This review summarises the original formulation of the Kalman filter, a tutorial introduction to it, a review of multisensor data fusion, and two orientation-estimation algorithms for inertial sensors (a nonlinear complementary filter and a gradient-descent algorithm). It describes the assumptions and trade-offs that the sources present. This is a narrative review; it contains no new data.