2011
DOI: 10.1109/tim.2011.2113120
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Performance Analysis of Kalman-Filter-Based Clock Synchronization in IEEE 1588 Networks

Abstract: Performances in network-based synchronization depend on several related factors, including the instability of local clocks, the rate at which timing information is exchanged, and the accuracy of the resulting correction estimates. This paper analyzes these effects and their relationships, showing how these may affect the design of an IEEE 1588 Precision Time Protocol synchronization scheme. This paper introduces a state-variable clock model for which realistic parameters can be obtained for different kinds of … Show more

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Cited by 152 publications
(99 citation statements)
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“…This section describes a discrete-time dynamic model to characterize the dynamics of the phase variations in h (n,m) (τ ). Based on the two-state models in [14], [15], we define the discrete-time state of the n th transmit node's carrier as x…”
Section: A Oscillator Dynamicsmentioning
confidence: 99%
“…This section describes a discrete-time dynamic model to characterize the dynamics of the phase variations in h (n,m) (τ ). Based on the two-state models in [14], [15], we define the discrete-time state of the n th transmit node's carrier as x…”
Section: A Oscillator Dynamicsmentioning
confidence: 99%
“…In our simulator, the slave clock is modeled by a "simple skew model" (SKM) introduced in [10]. In this model, the variations of offset and skew are considered as random processes and the dynamics of offset and skew are expressed as:…”
Section: B Slave Node's Inaccurate Clockmentioning
confidence: 99%
“…And the Kalman filter is a kind of hidden Markov model [11,12]. A Kalman filter may not always be realizable due to possible limitations of actual PTP devices, but it is equally of interest because it is known to provide a solution that, under suitable assumptions, can be considered optimal or near optimal [13]. In addition, the Kalman filter has many advantages: (i) lower mean error in tracking a Gaussian-distributed signal; (ii) lower computational overhead when the distribution is stationary; (iii) improve robustness in the presence of lost signals [7].…”
Section: Skew Estimating Model Based On Kalman Filtermentioning
confidence: 99%