Estimation of Photoacustic Signals Using Subspace Identification

https://doi.org/10.58571/CNCA.AMCA.2024.071

Resumen: This work presents a new estimation scheme for photoacoustic signals and the absorption profiles associated with these signals, based on a discrete linear time-invariant state-space model of the Stokes’ equation, which describes the propagation of ultrasound waves in acoustic attenuating media. Parameter estimation is performed using the N4SID method for subspace identification, taking advantage of discrete model properties. In addition, the gradient descent and regularization method is used to improve parameter estimation. The simulation results validate the scope of the proposed scheme.

¿Cómo citar?

Ginez Alvarez, A., Ramírez Chavarría, R.G. & Pérez Pacheco, A. (2024). Estimation of Photoacustic Signals Using Subspace Identification. Memorias del Congreso Nacional de Control Automático 2024, pp. 416-421. https://doi.org/10.58571/CNCA.AMCA.2024.071

Palabras clave

Photoacustic Signals, Absorption Profile Estimation, Subspace Identification, Linear State Space Model, Parameter and State Estimation

• Lang, O., Kovács, P., Motz, C., Huemer, M., Berer, T., and Burgholzer, P. (2019). A linear state space model for photoacoustic imaging in an acoustic attenuating media. Inverse Problems, 35(1), 015003.
• Ljung, L. (2013). Some classical and some new ideas for identification of linear systems. Journal of Control, Automation and Electrical Systems, 24(1), 3–10.Oversch
• ee, P.V. and Moor, B.D. (1994). N4sid: Subspace algorithms for the identification of combined deterministic-stochastic systems. Automatica, 30(1), 75–93.
• Overschee, P.V. and Moor, B.D. (1996). Subspace Identification for Linear Systems: Theory, Implementation, Applications. Kluwer Academic Publishers.
• Pérez-Pacheco, A., Ramírez-Chavarría, R.G., QuispeSiccha, R.M., and Colín-García, M.P. (2024). Dynamic modeling of photoacoustic sensor data to classify human blood samples. Medical & Biological Engineering & Computing, 62(2), 389–403.
• Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747.
• Veloz, G.M.R. (2023). Novel reconstruction algorithm for photoacoustic imaging: toward the quantitative imaging. Ph.D. thesis, Universidad de Guanajuato.