Towards a Model Based Sensor Measurement Variance Input for Extended Kalman Filter State Estimation
| Autor: | Frederic Bezombes, Christian Matthews, Benjamin J. McLoughlin, Harry A. G. Pointon |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
unmanned ground vehicle
0209 industrial biotechnology Unmanned ground vehicle Computer science lcsh:Motor vehicles. Aeronautics. Astronautics extended Kalman filter robotic total station Aerospace Engineering Ultra-wideband 02 engineering and technology Synthetic data Extended Kalman filter 020901 industrial engineering & automation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering state estimation Representation (mathematics) T1 Function (mathematics) Variance (accounting) Computer Science Applications TA Control and Systems Engineering Measuring instrument ultra wide band 020201 artificial intelligence & image processing lcsh:TL1-4050 Algorithm Information Systems |
| Zdroj: | Drones, Vol 3, Iss 1, p 19 (2019) Drones Volume 3 Issue 1 |
| ISSN: | 2504-446X |
| DOI: | 10.3390/drones3010019 |
| Popis: | In 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|