# Maximum Likelihood Estimation of ADC Parameters from Sine Wave Test Data. László Balogh, Balázs Fodor, Attila Sárhegyi, and István Kollár

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2 amplitude of the sine wave is only a few times (let us say, <3 times) larger than the ADC quantum size. In such cases, modifications (elimination of certain samples) can be used [4], [5]. When using the maximum lielihood estimation, these modifications are not necessary! Many times, the experimenter is also interested in the noise corrupting the samples. It would be nice to calculate this using the digital samples only. The observation is that noise causes oscillations between the neighboring output levels while the instantaneous value of the input sine is close to these. Thus, a measure of the length of these oscillations can be used to determine the noise level, and the centers of the oscillations can be used to determine the comparison levels. The paper analyses this and gives the estimates. The model is that the input is a sine wave, corrupted by Gaussian white noise, and this is quantized by an ADC. First the ADC is handled as ideal, but this is not a requirement. A typical output signal is illustrated in Figure. 4 3 Amplitude (in q) Time [s] Figure. Output samples of an ADC To obtain initial values, the length is measured at each level of the oscillations, and a weighted sum is formed. The result is corrected on the basis of a nonlinear corrections table [8]. Next, numerical solution of the maximization problem is suggested. Generally, the solution of the maximum lielihood problem is the maximization of the lielihood function L(a) to estimate the unnown parameters (a): M ( a) = max ( max L P Y i a) () a a As we will see later, in our case, the lielihood function is a product of probabilities. The distribution of the process is normal. Therefore, this problem is only solvable numerically. If the maximization is done, we can get the best parameters, because of the nice properties of the MLE (consistent, including asymptotically minimum-variance). The main message of this paper is how to handle this problem in the best way in order to extract possibly the full information from the measured data, and obtain a robust, effective algorithm. The presented method is quasi-static, we suppose, that the excitation signal has low frequency, and under the test conditions no dynamical effects appear (e.g out-of-range recovery). i= III. Modeling The first step of estimation is modeling. We suppose that the ADC is monotonic, and has no missing output codes. Local nonlinearities are possible, the true position of the transition levels have to be estimated. The ideal transfer function of an ADC can be seen in Figure.

3 N+ output (number of the output code) N N- N input (number of the transision level) Figure. Transfer function of an ideal ADC (N is the number of the transition levels, N = B, where B is the number of bits) When using sine wave test, the exciting signal is sinusoidal: ( tn + ϕ) + C = A cos( ωtn ) + B sin( tn ) C yn = Acos ω ω + () where y n is the n th output sample, A is the amplitude, ω is the frequency, C is the DC component, A, BB, C are the parameters of the exciting sine wave. The signal at the ADC input is corrupted by some noise. The assumed noise is additive, white, independent, zero-mean and Gaussian. One can say that the sine is the drift component of the noise. Therefore, the probability density function (pdf) of the noise is drifted in front of the transition levels of the ADC. This can be seen in Figure ,5,5,5 3 3,5 Input of the ADC (in q) Figure 3. Transition levels and the pdf of the input signal (vertical lines: transition levels, solid curve: pdf of the input signal, dotted curve: drifted pdf of the input signal, σ < q, where σ is the standard deviation of the additive noise, q is the quantum size) By the maximum lielihood estimation we should maximize the following joint probability: max L a ( a) = max P( Yi = yi ) M A, B, C, σ, q i= where L is the so-called lielihood function, a is the vector of the parameters, M is the number of the observations, Y i is the i th observed sample (random variable), y i is one of the output values (codes), σ is the standard deviation of the additive noise, q is the vector of the transition levels. The probability that the output code is the th (Y i = ) is the integral of the pdf between the comparison levels around the th output code (see Figure 3). The meaning of (3) is following: with proper setting of the model parameters, the product of the probabilities should be maximal. When we change the parameters, the Gaussian bell will be drifted, (3)

4 and the positions of the transition levels will also be changed. Each probability for the intermediate bins can be calculated as follows: q ( u y) σ σ π q e du (4) where q is the th comparison level, y is the model of the sine wave, and σ is the standard deviation of the additive noise. The integration limits for the overdriven bins are (-,q ] and [q N, ). Even if the data acquisition device is overdriven, the amplitude of the excitation sine wave can be calculated with help of these probabilities. Using the definition of the normal distribution function, expression (4) can be written as: q y q y Φ Φ. (5) σ σ This problem is solvable only numerically. To solve the ML problem, the lielihood function L(a) has to be maximized. Or equivalently, its logarithm logl(a) can be maximized. We can solve the maximization problem by minimizing the function -logl(a). Therefore, (3) becomes: min a M i= ( log P Y i = y i ) (6) IV. Numerical method The numerical minimization of (3) is a descent method. This repeatedly performs line search to get a minimum in the direction of the negative gradient vector. We have applied the so-called bactracing algorithm [9]. Assuming that the current parameter vector is a, a + is computed by a t a + = a + Δ (7) where t determines the step size, and the direction towards the minimum can be calculated as follows: Δa = ( L( a) ) log (8) a The method of bactracing line search is the following [9]: given a descent direction Δa for -logl(a), α (,.5), β (, ): a= a t := while -logl(a + tδa) > -logl(a) - αtδa T Δa, t := βt. The overall description of the algorithm which calls the gradient method as a subfunction is:. Calculate an initial value for a. while err(a, a - ) > ε.. Gradient method for only A, B, and C.. Gradient method for one by one the element of q.3. Gradient method for σ where err(a, a - ) is the maximal element of the absolute value of the error (minimax criterion), i.e. (, a ) = a a err a (9) and ε is a threshold for this error. A possible way to determine the initial parameters is using sine fitting, noise estimation algorithm [8] and uniform distribution of the transition levels. This can be explained as follows. Sine wave fitting [] is a good algorithm for determining the initial signal parameters. At the beginning of the test, we do not have any information about the nonlinearities of the ADC. Therefore, the best we can do is to uniformly partition of the code transition levels. To determine the initial value of the additive noise, the algorithm presented in [8] is used. This method uses the fact that the additive noise causes oscillations in the observations. The lengths of these oscillations are proportional to the standard

5 deviation of the additive noise. According to the algorithm, the lengths of the oscillations have to be measured. The central point of the oscillations is determined as the central of mass. In this case the mass is the probability of the oscillation between two output codes. Therefore, the center of the deviation is somewhere in the middle of the oscillation, where the oscillating probability is maximal. It is very similar to what the maximum lielihood method does: the method searches the set of the parameters so that the lielihood function (joint probability) is maximal. V. Simulation The estimation is realized in MATLAB. The true and the estimated transfer function can be seen in Figure 4. For the simulation, the true positions of the comparison levels are randomized (nonlinear behaviour), maintaining a monotonous transfer characteristic. The maximum of the relative error is about % Output [code] Input [V] Figure 4. The true (dotted line) and the estimated (solid line) transfer function of the ADC (B = 3bits, the ADC is monotonous but nonlinear) The noisy input signal and the estimated sine wave can be seen in Figure 5. One can see that the estimation wors very well. The true amplitude of the sine is, the estimated value is Amplitude [V] Time [us] x 4 Figure 5. The noisy input signal (grey curve) and the estimated sine wave (blac curve) (the standard deviation of the noise is.v) The estimated parameters have little error, except for the standard deviation of the noise. We hope that improvement of the algorithm will allow to decrease also this error, thus achieving a robust testing algorithm. We are going to investigate this for the conference presentation. VI. Conclusions

6 Maximum lielihood estimation has very nice properties. Our experience is that the sine wave test of the ADC s can be more robust with its help. An important question is the effectiveness. After some calculations we concluded that the Cramer-Rao bound is generally reached. References [] IEEE Standard 4-, IEEE Standard for Terminology and Test Methods for Analog-to- Digital Converters. The Institute of Electrical and Electronics Engineers. [] Press, W. H., B. P. Flannery, S. A. Teuolsy and W. T. Vetterling, Numerical Recipes: The Art of Scientific Computing. Cambridge University Press, Cambridge, 986. [3] J. J. Blair, Sine-fitting software for IEEE standards 57 and 4, in Proc. of the 6th IEEE Instrumentation and Measurement Technology Conference, IMTC/99, Venice, Italy, May 999, vol. 3, pp [4] Kollár and J. J. Blair, Improved Determination of the Best Fitting Sine Wave in ADC Testing IEEE Trans. on Instrumentation and Measurement, Vol. 54 No. 5, pp , 5. [5] I. Kollár, Improved Residual Analysis in ADC Testing 3th International Symposium on Measurements for Research and Industry Applications, 9th Worshop on ADC Modeling and Testing, Athens, Greece, Sep. 9t- Oct., [6] M. Fonseca da Silva, A. Cruz Serra, Improving Convergence of Sine Fitting Algorithms, Proc. 6th European Worshop on ADC Modeling and Testing, September 3-4,, Lisbon, Portugal. pp. -4. [7] B. Widrow, and I. Kollár, Quantization Noise, Boo in preparation, to be published by Cambridge University Press. Home Page URL: Chapter 6: Dither. [8] B. Fodor and I. Kollár, ADC Testing with Verification, Instrumentation and Measurement Technology Conference IMTC 7, Warsaw, Poland, May -3, 7. Submitted to the special issue of IEEE Trans. on Instrumentation and Measurement. [9] Stephen Boyd, Convex Optimization, Cambridge University Press, Cambridge, 4. [] A. Di Nisio, G. Cavone, N. Giaquinto, L. Fabbiano, M. Savino, Improved Static Testing of A/D Converters for DC Measurements, Instrumentation and Measurement Technology Conference IMTC 7, Warsaw, Poland, May -3, 7.

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