TY - JOUR
T1 - A Novel Robust Variational Bayesian Filter for Unknown Time-Varying Input and Inaccurate Noise Statistics
AU - Huang, Wei
AU - Fu, Hongpo
AU - Zhang, Weiguo
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2023/3/1
Y1 - 2023/3/1
N2 - Considering that, in many practical applications, the unknown time-varying input and heavy-tailed process and measurement noises induced by some unpredictable anomalous behaviors may degrade the performance of conventional filters seriously, this letter proposes a new robust variational Bayesian (VB) filter. First, the modified one-step prediction and measurement likelihood probability density function are constructed. Then, the VB method is utilized to jointly infer the system state, unknown time-varying input, and inaccurate noise covariance matrices. Finally, a new robust filter is derived, and its effectiveness is verified by the numerical simulations.
AB - Considering that, in many practical applications, the unknown time-varying input and heavy-tailed process and measurement noises induced by some unpredictable anomalous behaviors may degrade the performance of conventional filters seriously, this letter proposes a new robust variational Bayesian (VB) filter. First, the modified one-step prediction and measurement likelihood probability density function are constructed. Then, the VB method is utilized to jointly infer the system state, unknown time-varying input, and inaccurate noise covariance matrices. Finally, a new robust filter is derived, and its effectiveness is verified by the numerical simulations.
KW - heavy-tailed noise
KW - robust filter
KW - Sensor signal processing
KW - state estimation
KW - unknown input
KW - variational Bayesian
UR - https://www.scopus.com/pages/publications/85149417363
U2 - 10.1109/LSENS.2023.3248172
DO - 10.1109/LSENS.2023.3248172
M3 - 文章
AN - SCOPUS:85149417363
SN - 2475-1472
VL - 7
JO - IEEE Sensors Letters
JF - IEEE Sensors Letters
IS - 3
M1 - 7001104
ER -