Optimal recursive identification techniques from quantized outputs

Authors

  • Juan Carlos Gómez Universidad Nacional de Rosario
  • Gonzalo Daniel Sad CIFASIS, CONICET

DOI:

https://doi.org/10.52292/j.laar.2023.3279

Keywords:

Recursive Parameter Estimation, Quantized Data, Optimal Algorithms

Abstract

Recursive identification algorithms for the parameter estimation of linear systems from multilevel quantized outputs are introduced in this paper. The proposed algorithms are proved to be optimal in the sense that they minimize the a posteriori parameter estimation error covariance matrix. Numerical simulations are carried out to illustrate their performance for different quantization steps and Signal-to-Noise Ratios, as well as their capability to track time varying parameters.

Author Biography

Juan Carlos Gómez, Universidad Nacional de Rosario

Lab. for Syst. Dynamics and Signal Proc., FCEIA, Universidad Nacional de Rosario, 2000 Rosario, Argentina and CIFASIS

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Published

2023-07-18

Issue

Vol. 53 No. 4 (2023): Latin American Applied Research

Section

28th Congreso de la Asociacion Argentina de Control Automático

License

Copyright (c) 2023 Latin American Applied Research

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