Optimal Synchronization of Local Clocks by GPS 1PPS Signals Using Predictive FIR Filters

Arceo-Miquel, L.; Shmaliy, Yuriy S.; Ibarra-Manzano, Oscar

doi:10.1109/tim.2009.2013654

Cited by 60 publications

(23 citation statements)

References 22 publications

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“…Developments and applications of h 1 (n, 1) can be found in many papers [23][24][25][26][27][28]46], as predicting the next coming sample is of importance in many applications. An important special case of h 1 (n, −q) is associated with smoothing filtering with lag q = N − 1,…”

Section: Ramp Ufir Functionmentioning

confidence: 99%

“…A GPS-based clock synchronization structure utilizing the one-step predictive UFIR filter (18) with the ramp response h 1 (n, 1) has been designed in [46] as shown in Fig. 17 Figure 17.…”

Section: Clock Error Steering Using Predictive Ufir Structuresmentioning

confidence: 99%

“…17 Figure 17. Block-diagram of the GPS-based local clock synchronization loop utilizing the one-step predictive UFIR filter with the ramp response h1(n, 1) [46].…”

Section: Clock Error Steering Using Predictive Ufir Structuresmentioning

confidence: 99%

“…20. Different kinds of pseudolite clocks were selected and an UFIR synchronization algorithm [46] utilizing (18) was used as the clock synchronization algorithm. The results obtained turned out to be similar to those shown in Fig.…”

Section: Clock Error Steering Using Predictive Ufir Structuresmentioning

confidence: 99%

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Review of Unbiased FIR Filters, Smoothers, and Predictors for Polynomial Signals

Yuriy¹,

Yrjö²,

Khan³

2018

FSP

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Extracting an estimate of a slowly varying signal corrupted by noise is a common task. Examples can be found in industrial, scientific and biomedical instrumentation. Depending on the nature of the application the signal estimate is allowed to be a delayed estimate of the original signal or, in the other extreme, no delay is tolerated. These cases are commonly referred to as filtering, prediction, and smoothing depending on the amount of advance or lag between the input data set and the output data set. In this review paper we provide a comprehensive set of design and analysis tools for designing unbiased FIR filters, predictors, and smoothers for slowly varying signals, i.e. signals that can be modeled by low order polynomials. Explicit expressions of parameters needed in practical implementations are given. Real life examples are provided including cases where the method is extended to signals that are piecewise slowly varying. A critical view on recursive implementations of the algorithms is provided.

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Section: Ramp Ufir Functionmentioning

confidence: 99%

“…A GPS-based clock synchronization structure utilizing the one-step predictive UFIR filter (18) with the ramp response h 1 (n, 1) has been designed in [46] as shown in Fig. 17 Figure 17.…”

Section: Clock Error Steering Using Predictive Ufir Structuresmentioning

confidence: 99%

“…17 Figure 17. Block-diagram of the GPS-based local clock synchronization loop utilizing the one-step predictive UFIR filter with the ramp response h1(n, 1) [46].…”

Section: Clock Error Steering Using Predictive Ufir Structuresmentioning

confidence: 99%

Section: Clock Error Steering Using Predictive Ufir Structuresmentioning

confidence: 99%

See 2 more Smart Citations

Review of Unbiased FIR Filters, Smoothers, and Predictors for Polynomial Signals

Yuriy¹,

Yrjö²,

Khan³

2018

FSP

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“…Now expand x n to the finite l-degree 2 Taylor series (the higher degree terms are supposed to be identically zero) from n − N + 1 to n. It was shown in [19,22] that the unbiasedness betweenx n given as (1) and x n can be achieved, by (2), if one represents h ln (N ) with the l-degree polynomial h ln (N ) = l m=0 a ml (N )n m (3) Fig. 1 Low-degree polynomial impulse responses of unbiased FIR filters, after [19] and specifies the coefficient in (3) as…”

mentioning

confidence: 99%

Implementation of Digital Unbiased FIR Filters with Polynomial Impulse Responses

Castro-Tinttori

Ibarra-Manzano

Shmaliy

2011

Circuits Syst Signal Process

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This paper addresses the design and implementation of digital unbiased finite impulse response (FIR) filters with polynomial impulse response functions. The transfer function, its fundamental properties, and a general block-diagram are discussed for the impulse response represented with the l-degree Taylor series expansion. As a particular results, we show a fundamental identity uniquely featured to such filters in the transform domain. For low-degree impulse responses, the transfer functions are found in simple closed forms and represented in compact block-diagrams. The magnitude and phase responses are also analyzed along with the group delays. A comparison with predictive FIR filters is given. As examples of applications, filtering of time errors of local clocks is discussed along with the low-pass filter design employing a cascade of the unbiased FIR filters.

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References

2022

Optimal and Robust State Estimation

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Optimal Synchronization of Local Clocks by GPS 1PPS Signals Using Predictive FIR Filters

Cited by 60 publications

References 22 publications

Review of Unbiased FIR Filters, Smoothers, and Predictors for Polynomial Signals

Review of Unbiased FIR Filters, Smoothers, and Predictors for Polynomial Signals

Implementation of Digital Unbiased FIR Filters with Polynomial Impulse Responses

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