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UAV state estimation based on mixture generalized minimum error entropy smooth variable structure filter
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AI
DOI：10.1016/j.asr.2025.07.041.png)
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Abstract
En 中文
The strong maneuverability of Unmanned Aerial Vehicle (UAV) targets and the non-Gaussian characteristics of radar measurement noise can degrade the accuracy of state estimation methods reliant on precise motion models and Gaussian measurement noise assumptions, potentially leading to filter divergence. To address the challenges of UAV state estimation with uncertain motion models and non-Gaussian noise, this paper proposes a novel state estimation method based on the Mixture Generalized Minimum Error Entropy Smooth Variable Structure Filter (MGMEE-SVSF). The uncertainty error in motion model parameters is quantified by a time-varying Smooth Boundary Layer (SBL). If the norm of the SBL error exceeds a set threshold, the position is updated by the gain iteration of Mixture Generalized Minimum Error Entropy (MGMEE). Otherwise, the update is performed by the gain of Smooth Variable Structure Filter (SVSF), which has lower computational complexity. Finally, the proposed algorithm is compared with the Extended Kalman Filter (EKF), Minimum Error Entropy (MEE), MGMEE, and SVSF for various scenarios. Specifically, these comparisons are conducted in both Gaussian and non-Gaussian measurement noise conditions during UAV turns and mixed motion. The results demonstrate that the proposed state estimation algorithm offers better robustness and lower tracking errors compared to the other methods.
Keywords:
UAV state estimation
non-Gaussian noise
Mixture Generalized Minimum Error Entropy
Smooth Variable Structure Filter
uncertain motion models
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