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...
Main Authors: | Minhui Wang, Shuli Sun |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-10-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/19/20/4436 |
Similar Items
-
Self-Tuning Distributed Fusion Filter for Multi-Sensor Systems Subject to Unknown Model Parameters and Missing Measurement Rates
by: Guang-Quan Duan, et al.
Published: (2018-01-01) -
Extended Recursive Three-Step Filter for Linear Discrete-Time Systems with Dual-Unknown Inputs
by: Shigui Dong, et al.
Published: (2023-07-01) -
A class of admissible estimators of multiple regression coefficient with an unknown variance
by: Chengyuan Song, et al.
Published: (2020-07-01) -
Unknown Unknowns in Innovative Projects: Early Signs Sensemaking
by: Rosaria de Fatima Segger Macri Russo, et al.
Published: (2017-10-01) -
Distributed Optimal and Self-Tuning Filters Based on Compressed Data for Networked Stochastic Uncertain Systems with Deception Attacks
by: Yimin Ma, et al.
Published: (2022-12-01)