Analysis of the Performance of Four Filter Types for Drone Tracking
In this work, extensive simulations are done to compare the performance of the 4 filter types; Linear Kalman filter (LKF), Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF). A simple nearly constant velocity (NCV) motion model is used with a Gaussian noise measure...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , |
| منشور في: |
2023
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://depot.sorbonne.ae/handle/20.500.12458/1458 |
| الوسوم: |
إضافة وسم
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| الملخص: | In this work, extensive simulations are done to compare the performance of the 4 filter types; Linear Kalman filter (LKF), Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF). A simple nearly constant velocity (NCV) motion model is used with a Gaussian noise measurement model. Simulations were done with different ground truths, different measurements covariance matrices, and different speeds of the drone. Stone soup software was used in the simulations. The analyses revealed informative results that gave us more understanding of the behavior of the four filters when a common type of motion model such as the NCV model is used. |
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