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Instruments and Systems: Monitoring, Control, and Diagnostics

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Digital Analysis of the Spectral Composition based on the Calculation of the Periodogram Estimate of a Uniformly Sampled Signal Using Binary-Signed Analog-Stochastic Quantization
V.N. YAKIMOV

Binary-signed analog stochastic quantization is one of the promising areas that makes it possible to reduce the bit depth of digital signal samples
and synthesize computationally effi cient algorithms for spectral analysis. However, software components developed on the basis of these algorithms
cannot be used without the presence of special single-digit ADCs in signal processing systems, which limits their application in practice. The article
discusses the development of mathematical and algorithmic software for calculating the periodogram estimate of the power spectral density (PSD) in
the case of uniform sampling of a signal with imitation of binary-signed analog-stochastic quantization of its discrete samples. This approach made it
possible to preserve the specifi city of discrete-time data generation when using binary-signed analog-stochastic quantization. As a result, mathematical
software has been developed for calculating the periodogram estimate of the PSD, which does not require digital multiplication operations. The
consequence of this was an increase in the effi ciency of calculating periodogram estimates of the spectrum with uniform signal sampling. In addition,
the proposed method does not require a large amount of memory. This expands the possibility of its use in systems with a limit to the amount of
memory. The results of test experiments showed that the developed algorithms allow high frequency resolution spectral analysis of multicomponent
signals in the presence of white noise. A functionally independent software module has been developed based on these algorithms. It can be used as
part of metrology software for multifunctional measuring systems, which were originally focused on processing uniformly sampled signals.
Keywords: digital spectral analysis, Fourier transform, periodogram, binary stochastic quantization, fast algorithms

Pp. 08-16.

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