TY - GEN
T1 - Data-Driven State Estimation for Linear Systems
AU - Mishra, Vikas Kumar
AU - Hiremath, Sandesh Athni
AU - Bajcinca, Naim
N1 - Publisher Copyright:
© 2024 EUCA.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - We study the problem of estimating the states of a linear system based on measured data. We investigate the problem in both deterministic and stochastic settings. In the deterministic case, we develop data-driven conditions under which we can reconstruct state trajectories uniquely. Also, we discuss the case in which we have some missing data in the given input/output measurements. In the stochastic case, we develop a Kalman filter-like algorithm to recursively estimate both states and outputs. Finally, we consider a multi-input multi-output system to elucidate the developed results.
AB - We study the problem of estimating the states of a linear system based on measured data. We investigate the problem in both deterministic and stochastic settings. In the deterministic case, we develop data-driven conditions under which we can reconstruct state trajectories uniquely. Also, we discuss the case in which we have some missing data in the given input/output measurements. In the stochastic case, we develop a Kalman filter-like algorithm to recursively estimate both states and outputs. Finally, we consider a multi-input multi-output system to elucidate the developed results.
KW - behavioral system theory
KW - data-driven approach
KW - LTI systems
KW - missing data
KW - State reconstruction/estimation
UR - http://www.scopus.com/inward/record.url?scp=85200555587&partnerID=8YFLogxK
U2 - 10.23919/ECC64448.2024.10591180
DO - 10.23919/ECC64448.2024.10591180
M3 - Conference contribution
AN - SCOPUS:85200555587
T3 - 2024 European Control Conference, ECC 2024
SP - 906
EP - 913
BT - 2024 European Control Conference, ECC 2024
PB - Institute of Electrical and Electronics Engineers
T2 - 2024 European Control Conference, ECC 2024
Y2 - 25 June 2024 through 28 June 2024
ER -