Open AccessOptimal H 2 filtering for sampled‐data systems with measurement delays and packet dropouts
Author(s) -
Wang Wei,
Han Chunyan
Publication year - 2021
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/sil2.12021
Subject(s) - network packet , filtering problem , filter (signal processing) , control theory (sociology) , computer science , independent and identically distributed random variables , dropout (neural networks) , mathematics , riccati equation , estimator , algebraic riccati equation , bernoulli's principle , algorithm , filter design , statistics , differential equation , random variable , artificial intelligence , computer network , mathematical analysis , control (management) , machine learning , aerospace engineering , engineering , computer vision
This study examines the H 2 filtering problem for sampled‐data systems that are subject to measurement delays and packet dropouts. The phenomenon of packet dropout is different for each observation component and is described as an independent and identically distributed Bernoulli process, which is more general than the scalar packet dropout case. The reorganized observation technique is introduced herein to address the time delays in the observations. An optimal filter is constructed using a deterministic continuous‐time system with finite jumps, where the filter gains are determined by integrating a specific continuous‐time Riccati equation with finite jumps, based on a suitable algebraic Lyapunov equation. Compared with the discrete‐time filter, the optimal filter can provide the state estimates of the measurement sampling intervals and measurement sampling moments. Two simulation examples are explored to demonstrate the effectiveness of the proposed method.