TY - JOUR
T1 - Recursive Remote State Estimation for Stochastic Complex Networks with Degraded Measurements and Amplify-and-Forward Relays
AU - Liu, Tongjian
AU - Wang, Zidong
AU - Liu, Yang
AU - Wang, Rui
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025/1/6
Y1 - 2025/1/6
N2 - This paper is concerned with the remote state estimation problem for stochastic complex networks under the effects of degraded measurements and amplify-and-forward (AF) relays. Three sets of random variables are employed to describe the measurement degradation, the sensor transmission energy, and the relay transmission energy, respectively. The measurement from each node is transmitted to an AF relay and then forwarded to the remote estimator to facilitate the state estimation. A novel recursive estimator is constructed in the form of the extended Kalman filter. An upper bound of estimation error covariance is determined by solving Riccati-like difference equations based on the statistical information of the random variables, and such an upper bound is then minimized by choosing an appropriate estimator gain. Furthermore, sufficient conditions are established under which the estimation error is exponentially bounded in the sense of mean square. Finally, the effectiveness of the proposed estimation scheme is demonstrated by some numerical simulations.
AB - This paper is concerned with the remote state estimation problem for stochastic complex networks under the effects of degraded measurements and amplify-and-forward (AF) relays. Three sets of random variables are employed to describe the measurement degradation, the sensor transmission energy, and the relay transmission energy, respectively. The measurement from each node is transmitted to an AF relay and then forwarded to the remote estimator to facilitate the state estimation. A novel recursive estimator is constructed in the form of the extended Kalman filter. An upper bound of estimation error covariance is determined by solving Riccati-like difference equations based on the statistical information of the random variables, and such an upper bound is then minimized by choosing an appropriate estimator gain. Furthermore, sufficient conditions are established under which the estimation error is exponentially bounded in the sense of mean square. Finally, the effectiveness of the proposed estimation scheme is demonstrated by some numerical simulations.
KW - Complex networks
KW - state estimation
KW - Amplify-and-forward relay
KW - degraded measurements
KW - variance constraints
KW - amplify- and-forward relay
UR - http://www.scopus.com/inward/record.url?scp=85215380059&partnerID=8YFLogxK
U2 - 10.1109/TNSE.2025.3528768
DO - 10.1109/TNSE.2025.3528768
M3 - Article
JO - IEEE Transactions on Network Science and Engineering
JF - IEEE Transactions on Network Science and Engineering
SN - 2327-4697
ER -