Towards a Model Based Sensor Measurement Variance Input for Extended Kalman Filter State Estimation
AbstractIn this paper, we present an alternate method for the generation and implementation of the sensor measurement variance used in an Extended Kalman Filter (EKF). Furthermore, it demonstrates the limitations of a conventional EKF implementation and postulates an alternate form for representing the sensor measurement variance by extending and improving the characterisation methodology presented in the previous work. As presented in earlier work, the use of surveying grade optical measurement instruments allows for a more effective characterisation of Ultra-Wide Band (UWB) localisation sensors; however, in cluttered environments, the sensor measurement variance will change, making this method not robust. To compensate for the noisier readings, an EKF using a model based sensor measurement variance was developed. This approach allows for a more accurate representation of the sensor measurement variance and leads to a more robust state estimation system. Simulations were run using synthetic data in order to test the effectiveness of the EKF against the originally developed EKF; next, the new EKF was compared to the original EKF using real world data. The new EKF was shown to function much more stably and consistently in less ideal environments for UWB deployment than the previous version. View Full-Text
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Pointon, H.A.G.; McLoughlin, B.J.; Matthews, C.; Bezombes, F.A. Towards a Model Based Sensor Measurement Variance Input for Extended Kalman Filter State Estimation. Drones 2019, 3, 19.
Pointon HAG, McLoughlin BJ, Matthews C, Bezombes FA. Towards a Model Based Sensor Measurement Variance Input for Extended Kalman Filter State Estimation. Drones. 2019; 3(1):19.Chicago/Turabian Style
Pointon, Harry A.G.; McLoughlin, Benjamin J.; Matthews, Christian; Bezombes, Frederic A. 2019. "Towards a Model Based Sensor Measurement Variance Input for Extended Kalman Filter State Estimation." Drones 3, no. 1: 19.
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