TY - GEN
T1 - A Co-estimation Algorithm Based on Adaptive Residual Generator for Multi-sinusoidal Signals
AU - Xu, Xiaoyi
AU - Li, Xianling
AU - Ke, Zhiwu
AU - Luo, Hao
AU - Huo, Mingyi
AU - Jiang, Yuchen
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - This paper presents an adaptive residual generator-based co-estimation algorithm for multi-sinusoidal signals with unknown components. By modeling the multi-sinusoidal signal, a residual generator algorithm integrated with the adaptive estimation mechanism is constructed. The proposed algorithm can simultaneously estimate the amplitude, frequency, and phase of every sinusoidal component online with stability and convergence guarantees. The performance of the proposed co-estimation approach is evaluated with a simulation experiment.
AB - This paper presents an adaptive residual generator-based co-estimation algorithm for multi-sinusoidal signals with unknown components. By modeling the multi-sinusoidal signal, a residual generator algorithm integrated with the adaptive estimation mechanism is constructed. The proposed algorithm can simultaneously estimate the amplitude, frequency, and phase of every sinusoidal component online with stability and convergence guarantees. The performance of the proposed co-estimation approach is evaluated with a simulation experiment.
KW - adaptive residual generator
KW - co-estimation
KW - multi-sinusoidal signals
UR - https://www.scopus.com/pages/publications/105032658987
U2 - 10.1109/INDIN64977.2025.11279177
DO - 10.1109/INDIN64977.2025.11279177
M3 - 会议稿件
AN - SCOPUS:105032658987
T3 - IEEE International Conference on Industrial Informatics (INDIN)
BT - 2025 IEEE 23rd International Conference on Industrial Informatics, INDIN 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd International Conference on Industrial Informatics, INDIN 2025
Y2 - 12 July 2025 through 15 July 2025
ER -