Adaptive Tuning of Acoustic Positioning Sensor
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This thesis compare methods of noise covariance estimation with the aim to improve the percision of Extended Kalman Filter (EKF) estimates of acoustic distance measurements. Because computational load is a priority, only suboptimal estimators have been considered; four relying on innovation covarianse matching and one sequential Maximum Likelihood Estimator. The methods will mainly be compared by the mean positional error, the filter state estimate covariance, and the estimators ability to hit the target covariance. A simulated HiPAP acoustic measurement signal was used as the measurement for the adaptive filters. The methods performance and sources of errors have been discussed. Simulations show that the process noise covariance is needed to be known or estimated to ensure performance during noise covariance estimation. Moreover simulations show that the proposed combination estimator outperforms the established methods, if trained on measurements under static noise conditions.