How to optimize unscented kalman filter tuning?
Best insight from top research papers
To optimize the tuning of an Unscented Kalman Filter (UKF), a Two-Stage Bayesian Optimization (TSBO) methodology can be used. This involves using a t-Student Process to optimize the process noise parameters of the UKF . The TSBO aims to find a location in the feasible region that maximizes the probability of improving the current best solution . By minimizing performance metrics, such as the average sum of the states' and measurement' estimation error, the UKF can be tuned for various vehicle maneuvers and behaviors . Another approach is to use a multi-objective function genetic algorithm to find the optimal values of state and noise covariance matrices . This optimization process aims to minimize the mean square errors between the actual and estimated values of speed, current, and flux . Overall, these optimization techniques can improve the estimation performance of the UKF in different applications .
Answers from top 5 papers
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| Papers (5) | Insight |
|---|---|
| The paper proposes using Bayesian optimization (BO) to auto-tune the parameters of unscented Kalman filters (UKF) for optimal performance in AUV navigation. | |
| The paper uses a multi-objective function genetic algorithm to find the optimal values of state and noise covariance matrices for optimizing the unscented Kalman filter tuning. | |
07 Apr 2023 | The paper discusses the optimization of unscented Kalman filter tuning using Bayesian optimization (BO) for open and closed-loop performance. |
05 Jun 2022 | The paper proposes a Two-Stage Bayesian Optimization (TSBO) method to optimize the tuning of an Unscented Kalman Filter (UKF) for vehicle sideslip angle estimation. |
05 Jun 2022 | The paper proposes a Two-Stage Bayesian Optimization (TSBO) method to optimize the process noise parameters of an Unscented Kalman Filter (UKF) for vehicle sideslip angle estimation. |
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