Reducing Extended Kalman Filter Sampling Rates for Multi-Rate Fusion through Computationally Efficient Sequential Single Sensor Measurement Processing

Real-time, high-rate Extended Kalman Filter (EKF) execution must include considerations of its computational load, which can pose a challenge for the implementation depending on the specific observer rate. While methods for the reduction of the computational load exist, this paper seeks to circumvent the problem, by reducing the EKF sampling rate. This is explored for the case of multi-rate sensor fusion for drive control applications where at least one sensor sampling rate exceeds the control cycle rate. A lower rate EKF is implemented where, in contrast to a single-rate EKF approach, none of the higher rate measurements are neglected, but instead collected and sequentially processed during each EKF execution. Two formulations based on this concept are introduced. The first optimises the estimation error, accepting a significant increase in computational load, while the second seeks the best compromise between estimation error and computational load.