New ways to visualize time and frequency data

摘要

Some of the standards of numerical analysis in time and frequency are the formation of the various square roots of variances, such as Time Deviation (TDEV), Allan Deviation (ADEV), and Total Deviation (TotDev), among others. As time and frequency measurements and transfer becomes better and better, especially at smaller sampling intervals, transient disturbances from such things as environmental perturbations become more and more important to characterize, locate, and understand. While developing software tools to more fully analyze, visualize, and model time and frequency data, especially time transfer data, several "new" ways of looking at the data were tested for usefulness. One new way of looking at time-series data was first reported in 1987 and is called visual recurrence plots or analysis (VRA) (1). VRA, the auto-correlation function (ACF), power spectral density by the Barnes' Digital Spectrum Analyzer method (2) (PSD), periodogram using phase-dispersion-minimization techniques (Jurkevich(7)), phase plane visualization (PPV), time-frequency analysis (TFA), and even 1-D wavelet decomposition of a time-series signal are being tested. This paper will show some recent results that show that all these numerical tools are useful. Tests will be run on both real and synthetic data.