Cargando…
Self-Tuning Distributed Fusion Filter for Multi-Sensor Networked Systems with Unknown Packet Receiving Rates, Noise Variances, and Model Parameters
In this study, we researched the problem of self-tuning (ST) distributed fusion state estimation for multi-sensor networked stochastic linear discrete-time systems with unknown packet receiving rates, noise variances (NVs), and model parameters (MPs). Packet dropouts may occur when sensor data are s...
Autores principales: | Wang, Minhui, Sun, Shuli |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6832661/ https://www.ncbi.nlm.nih.gov/pubmed/31614955 http://dx.doi.org/10.3390/s19204436 |
Ejemplares similares
-
Hybrid Adaptive Cubature Kalman Filter with Unknown Variance of Measurement Noise
por: Shi, Yuepeng, et al.
Publicado: (2018) -
Sequential Fusion Filter for State Estimation of Nonlinear Multi-Sensor Systems with Cross-Correlated Noise and Packet Dropout Compensation
por: Tan, Liguo, et al.
Publicado: (2023) -
Robust Fusion Kalman Estimator of the Multi-Sensor Descriptor System with Multiple Types of Noises and Packet Loss
por: Zheng, Jie, et al.
Publicado: (2023) -
Tracking an Underwater Object with Unknown Sensor Noise Covariance Using Orthogonal Polynomial Filters
por: Kumar, Kundan, et al.
Publicado: (2022) -
Sequential Covariance Intersection Fusion Robust Time-Varying Kalman Filters with Uncertainties of Noise Variances for Advanced Manufacturing
por: Qi, Wenjuan, et al.
Publicado: (2022)