Abstract
Sine signals are used widely in many application. This paper presents a fast signal parameter identification algorithm in terms of the feature parameters of the sine wave with an initial phase. In order to avoid complex calculation and realize fast parameter identification, a multiple three-point identification technique is developed by constructing algebraic equation group based on three discrete observations and solving equations. Moreover, to overcome the difficulty of solving transcendental equations regarding the signal parameters, the original transcendental equation group is transformed into a simple form through a equation transformation. Finally, an example is provided to test the performance of the proposed signal identification method and the simulation results show nice performance.
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References
Lin, Y., Zhang, Y., Fu, S., Zhang, H., Wang, P.: A configurable detection chip with 0.6% Inaccuracy for liquid conductivity using dual-frequency sinusoidal signal technique in 65 nm CMOS. Microelectron. J. 124, 105434 (2022)
Tehrani, O.S., Sabahi, M.F.: Eigen analysis of flipped Toeplitz covariance matrix for very low SNR sinusoidal signals detection and estimation. Digit. Sig. Proc. 129, 103677 (2022)
Ding, F., Xu, L., Liu, X.M.: Signal modeling—Part A: Single-frequency signals. J. Univ. Sci. Technol. (Nat. Sci. Ed.) 38(1), 1–13 (2017)
Dastres, H., Ebrahimi, S.M., Malekzadeh, M., Gordillo, F.: Robust adaptive parameter estimator design for a multi-sinusoidal signal with fixed-time stability and guaranteed prescribed performance boundary of estimation error. J. Franklin Inst. 360(1), 223–250 (2023)
Liu, T., Huang, J.: Global exponential estimation of the unknown frequencies of discrete-time multi-tone sinusoidal signals. Automatica 142, 110377 (2022)
Xu, L., Ding, F., Zhu, Q.M.: Separable synchronous multi-innovation gradient based iterative signal modeling from online measurements. IEEE Trans. Instrum. Meas. 71, 6501313 (2022)
Jiang, T., Xu, D., Chen, T., Sheng, A.: Parameter estimation of discrete-time sinusoidal signals: a nonlinear control approach. Automatica 109, 108510 (2019)
Pin, G., Wang, Y., Chen, B., Parisini, T.: Identification of multi-sinusoidal signals with direct frequency estimation: an adaptive observer approach. Automatica 99, 338–345 (2019)
Pin, G., Chen, B., Parisini, T.: Robust finite-time estimation of biased sinusoidal signals: a Volterra operators approach. Automatica 77, 120–132 (2017)
Ding, F.: System Identification—New Theory and Methods. Science Press, Beijing (2013)
Ding, F.: System Identification—Performance Analysis for Identification Methods. Science Press, Beijing (2014)
Ding, F.: System Identification—Auxiliary Model Identification Idea and Methods. Science Press, Beijing (2017)
Ding, F., Yang, J.B., Xu, Y.M.: Convergence of hierarchical stochastic gradient identification for transfer function matrix models. Control Theory Appl. 18(6), 949–953 (2001)
Ding, F., Yang, J.B.: Hierarchical identification of large scale systems. Acta Automatica Sin. 25(5), 647–654 (1999)
Ljung, L.: System Identification Theory for the User. Prentice Hall (1999)
Acknowledgements
This work was supported by Qing Lan Project of Jiangsu Province, by the “333” Project of Jiangsu Province (No. BRA2018328). The authors are grateful to Professor Feng Ding at Jiangnan University for his helpful suggestions.
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Xu, L., Xu, W., Ding, F. (2023). Fast Parameter Estimation Algorithm for the Signal Modeling Based on Equation Solution. In: Jia, Y., Zhang, W., Fu, Y., Wang, J. (eds) Proceedings of 2023 Chinese Intelligent Systems Conference. CISC 2023. Lecture Notes in Electrical Engineering, vol 1089. Springer, Singapore. https://doi.org/10.1007/978-981-99-6847-3_20
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DOI: https://doi.org/10.1007/978-981-99-6847-3_20
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