Particle-Based Tuning of the Unscented Kalman Filter

Abstract

The unscented Kalman filter (UKF) is one of the most used approximate solutions to the problem of nonlinear filtering. It is relatively easy to implement, and it produces better state estimates than the extended Kalman filter, especially when the nonlinearities of the dynamic system are significant. The quality of the estimates yielded by the UKF is dependent on the tuning of the parameters that govern the unscented transform (UT). To the user, manually tuning the UT means picking proper values for three scalar variables with almost no theoretical guidance. To help relieve the user from this burden, we approach the tuning of the UT parameters as an optimization problem and propose a tuning algorithm based on ideas of the bootstrap particle filter. The proposed algorithm is analyzed and numerically tested against both a set of popular nonlinear filters and a recently published model-based tuning algorithm.