Abstract :
A new method for ADC characterization, based on singular value decomposition, is introduced in this paper. Here, the singular values of the sampled data matrix, directly derived from the measured input data, are used to characterize the signal-to-noise ratio (SNR), and further to estimate the number of effective bits. Various input signals, such as single-tone, dual-tone, or multitone, can be used to obtain accurate estimation results. In addition, the new method avoids the difficulties and problems of the earlier characterization methods such as the sensitivity to the applied sinewave frequency and sampled data sizes, and the spectral leakage. Extensive simulations indicate that the proposed method provides excellent performance with single-tone, dual-tone, and multitone test signals. The proposed method also shows remarkable robustness over a truly wide SNR range: from 5 dB to 200 dB
Keywords :
analogue-digital conversion; eigenvalues and eigenfunctions; singular value decomposition; spectral analysis; white noise; ADC characterization; Frobenius norm; Nyquist frequency; SNR; data conversion resolution; dual-tone signals; eigenvalue method; multitone signals; noisy data matrix; number of effective bits; random white noise; sampled data matrix; single-tone signals; singular value decomposition; Discrete Fourier transforms; Frequency; Matrix decomposition; Performance evaluation; Robustness; Sampling methods; Signal to noise ratio; Singular value decomposition; Spectral analysis; Testing;
