A maximum likelihood (ML) estimator is derived for the problem of measuring the code transition levels of an analog-to-digital converter (ADC). The proposed method is intended to test the linearity of the ADC in the static regime, using only constant test signals, except for a small amount of additive noise. The measurement data are employed in a nearly optimal manner, due to the statistical properties of the ML estimator, which are thoroughly examined. The reported analysis allows the design of the test under a given uncertainty constraint. (C) 2009 Elsevier B.V. All rights reserved.
Maximum likelihood estimation for linearity testing of ADCs stimulated by known constant signals / Di Nisio, Attilio; Fabbiano, Laura; Giaquinto, Nicola; Savino, Mario. - In: COMPUTER STANDARDS & INTERFACES. - ISSN 0920-5489. - STAMPA. - 32:3(2010), pp. 119-125. [10.1016/j.csi.2009.11.006]
Maximum likelihood estimation for linearity testing of ADCs stimulated by known constant signals
Di Nisio, Attilio;Fabbiano, Laura;Giaquinto, Nicola;Savino, Mario
2010-01-01
Abstract
A maximum likelihood (ML) estimator is derived for the problem of measuring the code transition levels of an analog-to-digital converter (ADC). The proposed method is intended to test the linearity of the ADC in the static regime, using only constant test signals, except for a small amount of additive noise. The measurement data are employed in a nearly optimal manner, due to the statistical properties of the ML estimator, which are thoroughly examined. The reported analysis allows the design of the test under a given uncertainty constraint. (C) 2009 Elsevier B.V. All rights reserved.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.