Kalman Filter Tuning with Bayesian Optimization

Understand

Many state estimation algorithms must be tuned given the state space process and observation models, the process and observation noise parameters must be chosen.

• Conventional tuning approaches rely on heuristic hand-tuning or gradient-based optimization techniques to minimize a performance cost function.
• However, the relationship between tuned noise values and estimator performance is highly nonlinear and stochastic.
• Therefore, the tuning solutions can easily get trapped in local minima, which can lead to poor choices of noise parameters and suboptimal estimator performance.