Cargando…
Noise-Adaption Extended Kalman Filter Based on Deep Deterministic Policy Gradient for Maneuvering Targets
Although there have been numerous studies on maneuvering target tracking, few studies have focused on the distinction between unknown maneuvers and inaccurate measurements, leading to low accuracy, poor robustness, or even divergence. To this end, a noise-adaption extended Kalman filter is proposed...
Autores principales: | Li, Jiali, Tang, Shengjing, Guo, Jie |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9320134/ https://www.ncbi.nlm.nih.gov/pubmed/35891067 http://dx.doi.org/10.3390/s22145389 |
Ejemplares similares
-
A Novel Adaptive Robust Cubature Kalman Filter for Maneuvering Target Tracking with Model Uncertainty and Abnormal Measurement Noises
por: Ye, Xiangzhou, et al.
Publicado: (2023) -
Hybrid Adaptive Cubature Kalman Filter with Unknown Variance of Measurement Noise
por: Shi, Yuepeng, et al.
Publicado: (2018) -
Measurement Noise Covariance-Adapting Kalman Filters for Varying Sensor Noise Situations
por: Chhabra, Anirudh, et al.
Publicado: (2021) -
Dynamical Motor Control Learned with Deep Deterministic Policy Gradient
por: Shi, Haibo, et al.
Publicado: (2018) -
End-to-End Automated Lane-Change Maneuvering Considering Driving Style Using a Deep Deterministic Policy Gradient Algorithm
por: Hu, Hongyu, et al.
Publicado: (2020)