Strong Tracking PHD Filter Based on Variational Bayesian with Inaccurate Process and Measurement Noise Covariance

Assuming that the measurement and process noise covariances are known, the probability hypothesis density (PHD) filter is effective in real-time multi-target tracking; however, noise covariance is often unknown and time-varying for an actual scene. To solve this problem, a strong tracking PHD filter...

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Detalles Bibliográficos
Autores principales: Hu, Zhentao, Yang, Linlin, Jin, Yong, Wang, Han, Yang, Shibo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7916040/
https://www.ncbi.nlm.nih.gov/pubmed/33562792
http://dx.doi.org/10.3390/s21041126

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7916040/
https://www.ncbi.nlm.nih.gov/pubmed/33562792
http://dx.doi.org/10.3390/s21041126

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