Biomedical subjects

Yuriy S Shmaliy

Publications and source records attributed to Yuriy S Shmaliy.

5 recordsLinked to original sources

PubMed2006

An unbiased FIR filter for TIE model of a local clock in applications to GPS-based timekeeping.

An unbiased finite impulse response (FIR) filter is proposed to estimate the time-interval error (TIE) K-degree polynomial model of a local clock in global positioning system (GPS)-based timekeeping in the presence of noise that is not obligatory Gaussian. Generic coefficients for the unbiased FIRs are derived. The low-degree FIRs and noise power gains are given. An estimation algorithm is proposed and examined for the TIE measurements of a crystal clock in the presence of the uniformly distributed sawtooth noise induced by the multichannel GPS timing receiver. Based upon this algorithm, we show that the unbiased FIR estimates are consistent with the reference (rubidium) measurements and fit them better than the standard Kalman filter.

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PubMed2005

Approximate estimates of limiting errors of passive wireless SAW sensing with DPM.

This paper discusses approximate statistical estimates of limiting errors associated with single differential phase measurement of a time delay (phase difference) between two reflectors of the passive surface acoustic wave (SAW) sensor. The remote wireless measurement is provided at the ideal coherent receiver using the maximum likelihood function approach. Approximate estimates of the mean error, mean square error, estimate variance, and Cramér-Rao bound are derived along with the error probability to exceed a threshold in a wide range of signal-to-noise ratio (SNR) values. The von Mises/Tikhonov distribution is used as an approximation for the phase difference and differential phase diversity. Simulation of the random phase difference and limiting errors also is applied.

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PubMed2004

One-port noise model of a crystal oscillator.

This paper presents a one-port noise model of a crystal oscillator combined with equivalent impedances of a resonator and linearized feedback amplifier. Based on the noise conversion technique, we translate the thermal additive and flicker noises of both the resonator and amplifier into the oscillator signal amplitude and phase. The generic transformation coefficients for the noise are derived, and the power spectral density (psd) function of the oscillator signal phase is analyzed in detail. The remarkable property of the model is demonstrated by determining the separate contribution of each noise source to the oscillator performance. Some important rules for shaping the phase psd are noted. The consistency with Leeson's model also is reported.

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PubMed2004

The noise conversion method for oscillatory systems.

The paper addresses a method for calculating the amplitude and phase power spectral density (PSD) functions of an oscillatory system (resonator, oscillator, bandpass filter, selective circuit, etc.) via the PSDs of its intrinsic noise sources and relevant transformation coefficients. A systematic description of the method is given for the scalar and vector noises. As an illustration, the noise transformation coefficients are derived for a piezoelectric series branch with fluctuating motional inductance, capacity, and losses, in which static capacity is disregarded. We then clarify the rules regarding the shaping of either PSD function. The importance of this method resides in the fact that it enables us to study particular finite ranges of the PSD function without using differential equations.

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PubMed2002

A simple optimally unbiased MA filter for timekeeping.

In this paper, we address the design of an optimally unbiased moving average (MA) filter for time error online estimating and synchronization in Global Positioning System (GPS)-based timekeeping. We proceed from the slowly changing nature of a time error generated by a local clock, assuming its function to be linear for N points in the average. Employing the inherent property of a simple MA to produce the lowest noise for the given averaging interval and using linear regression as a proper stochastic approximation to evaluate the bias, we find the optimal weighting coefficients. The designed optimally unbiased MA filter and its improved modification produce two times more noise than a simple MA when N >> 1. Both the numerical simulation and the filtering of the real GPS-based time error processes are provided and discussed in detail.

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