Phase Noise Analysis of Time Transfer over White Rabbit-Network Based Optical Fibre Links

White Rabbit (WR) is an optical fibre-based time-frequency synchronization technology typically used in timekeeping laboratories for distributing time-frequency signals from a reference clock to distant locations. The accuracy of the received signals at the user end can be affected by random noise processes present in the WR network due to the internal electronic components of WR devices. In this paper, we investigate the presence of random noise processes in the WR network. We then study their statistical properties and model the distribution based on experimentally recorded measurements. According to our study, the probability density function (PDF) follows a Gaussian mixture model (GMM) with varying distribution parameters, and the correlation analysis indicates a strong correlation of the phase noise process over the temporal samples. Furthermore, the developed phase noise models have also been verified by comparing them against additional experimental data. Finally, we present the methodology to generate the phase noise process using computer simulations with the PDF and correlation models developed in this work to help algorithm developers and equipment manufacturers make use of our results.

On the Noise Complexity in an Optical Motion Capture Facility

Optical motion capture systems are state-of-the-art in motion acquisition; however, like any measurement system they are not error-free: noise is their intrinsic feature. The works so far mostly employ a simple noise model, expressing the uncertainty as a simple variance. In the work, we demonstrate that it might be not sufficient and we prove the existence of several types of noise and demonstrate how to quantify them using Allan variance. Such a knowledge is especially important for using optical motion capture to calibrate other techniques, and for applications requiring very fine quality of recording. For the automated readout of the noise coefficients, we solve the multidimensional regression problem using sophisticated metaheuristics in the exploration-exploitation scheme. We identified in the laboratory the notable contribution to the overall noise from white noise and random walk, and a minor contribution from blue noise and flicker, whereas the violet noise is absent. Besides classic types of noise we identified the presence of the correlated noises and periodic distortion. We analyzed also how the noise types scale with an increasing number of cameras. We had also the opportunity to observe the influence of camera failure on the overall performance.

Military and industrial performance: the critical role of noise controls

Noise control is a well understood and important engineering skill. The science has been developed to address operational needs of being quiet on the one hand, and avoiding hearing loss on the other, both in industry and military operations. Noise control is also the first priority step in systems safety risk mitigation for noise hazards, as evidenced in U.S. industry by the requirement stated in Federal OSHA regulation 1910.95: "(b)(1) When employees are subjected to sound exceeding those listed in Table G-16, feasible administrative or engineering controls shall be utilized." In actual practice, engineering controls are of first preference, while the second step is administrative noise controls, reducing noise exposures by removing personnel from high-noise environments. The third is the use of personal protective equipment (PPE), commonly known as earmuffs and earplugs. Each of these topics is discussed herein. The U.S. Navy has developed and/or implemented many groundbreaking noise control efforts on ships, and that provides the basis of discussion in this article. This article, as an overview of noise control, also addresses issues associated with high-noise environments and consideration of noise control techniques.