228 / 2021-04-09 16:22:12

Automated tuning of Kalman based virtual sensors for full-field acoustic pressure

Estimation,Kalman filter,acoustic testing,covariances,vibration

This paper presents a new method for automated tuning of Kalman based virtual sensors. Such virtual sensors use a Kalman filter to estimate non-measured quantities, based on measured data and a model. In order to achieve optimum accuracy, one must characterize the model prediction errors and the measurement errors by means of their respective covariance matrices. The latter determine the relative weighting of model information against measurement data, i.e. the tuning of the Kalman filter. Because the quantification of the model prediction error covariances is particularly difficult, many applications rely on tedious and sub-optimal manual tuning. This work proposes an alternative. It applies to linear oscillatory systems, as found in many structural or acoustic applications. The method provides optimized model error covariance values in a fast and automated manner, based on sensor datasheet info and steady state data from sensors that would already be required by the virtual sensor. It is validated experimentally on a mock-up of a direct field acoustic test setup. There, a Kalman based virtual sensor for the full pressure field is tuned. The validation shows that the proposed method achieves close-to-optimal accuracy, in a variety of studied cases with different model accuracies.