The estimation of Line Spectral Frequencies trajectories based on Unscented Kalman Filtering

Boubakir, Chabane; Berkani, Daoud

doi:10.1109/ssd.2009.4956767

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…where µ x is the predicted mean, P xx is the predicted covariance, Q is the process noise covariance, and β is used to incorporate prior knowledge of the distribution of x [19].…”

Section: Unscented Kalman Filter Algorithmmentioning

confidence: 99%

A New Variance-Covariance Matrix for Improving Positioning Accuracy in High-Speed GPS Receivers

Rahemi

Mosavi

Martín

2021

Sensors

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One of the main challenges in using GPS is reducing the positioning accuracy in high-speed conditions. In this contribution, by considering the effect of spatial correlation between observations in estimating the covariances, we propose a model for determining the variance–covariance matrix (VCM) that improves the positioning accuracy without increasing the computational load. In addition, we compare the performance of the extended Kalman filter (EKF) and unscented Kalman filter (UKF) combined with different dynamic models, along with the proposed VCM in GPS positioning at high speeds. To review and test the methods, we used six motion scenarios with different speeds from medium to high and examined the positioning accuracy of the methods and some of their statistical characteristics. The simulation results demonstrate that the EKF algorithm based on the Gauss–Markov model, along with the proposed VCM (based on the sinusoidal function and considering spatial correlations), performs better and provides at least 30% improvement in the positioning, compared to the other methods.

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“…where µ x is the predicted mean, P xx is the predicted covariance, Q is the process noise covariance, and β is used to incorporate prior knowledge of the distribution of x [19].…”

Section: Unscented Kalman Filter Algorithmmentioning

confidence: 99%