This paper introduces a new estimator of the parameters of a sinewave based on the sequence of its quantized values and on the knowledge of the quantizer transition levels. The estimator performance is evaluated against that of the well-established three-parameter sinefit technique. It is shown that the new estimator provides a significantly higher accuracy, which even approaches the noise floor. This is achieved by exploiting the additional information on the nonlinear behavior of the quantizer. Detailed simulations are performed to reveal the statistical properties of the proposed estimator. In addition, a sensitivity analysis is also carried out to demonstrate the robustness of the method against the uncertainty in the knowledge of the transition levels.
Calibrated Sinefit Based on Quantized Data
Carbone P.;De Angelis A.;Moschitta A.
2024
Abstract
This paper introduces a new estimator of the parameters of a sinewave based on the sequence of its quantized values and on the knowledge of the quantizer transition levels. The estimator performance is evaluated against that of the well-established three-parameter sinefit technique. It is shown that the new estimator provides a significantly higher accuracy, which even approaches the noise floor. This is achieved by exploiting the additional information on the nonlinear behavior of the quantizer. Detailed simulations are performed to reveal the statistical properties of the proposed estimator. In addition, a sensitivity analysis is also carried out to demonstrate the robustness of the method against the uncertainty in the knowledge of the transition levels.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.