Characterizing Periodic Variations of Atomic Frequency Standards via Their Frequency Stability Estimates.

The onboard atomic frequency standard (AFS) is a crucial element of Global Navigation Satellite System (GNSS) satellites. However, it is widely accepted that periodic variations can influence the onboard AFS. The presence of non-stationary random processes in AFS signals can lead to inaccurate separation of the periodic and stochastic components of satellite AFS clock data when using least squares and Fourier transform methods.

Constrained MLAMBDA Method for Multi-GNSS Structural Health Monitoring.

Deformation monitoring of engineering structures using the advanced Global Navigation Satellite System (GNSS) has attracted research interest due to its high-precision, constant availability and global coverage. However, GNSS application requires precise coordinates of points of interest through quick and reliable resolution of integer ambiguities in carrier phase measurements. Conventional integer ambiguity resolution algorithms have been extensively researched indeed in the past few decades, although the application of GNSS to structural health monitoring is still limited.

Predicting Long-Term Frequency Stability: Stochastic Oscillator Noise Analysis.

High-performance frequency standards are affected by power-law noises and characterized by structure functions (i.e., variances).

Predicting Long-Term Stability of Precise Oscillators under Influence of Frequency Drift.

High-performance oscillators, atomic clocks for instance, are important in modern industries, finance and scientific research. In this paper, the authors study the estimation and prediction of long-term stability based on convex optimization techniques and compressive sensing. To take frequency drift into account, its influence on Allan and modified Allan variances is formulated.