A quick algorithm to compute an approximated power spectral density from an arbitrary Allan deviation

Complex architectures for wireless communications, digital electronics and space-based navigation interlink several oscillator-based devices such as clocks, transponders and synthesizers. Estimators characterizing their stability are critical for addressing the impact of random fluctuations (noise) on the overall system performance. Manufacturers typically specify this as an Allan/Hadamard Variance (AVAR/HVAR) profile in the _integration_ time domain, yet, stochastic processes governing the noise take place in the _Fourier_ frequency domain in the shape of a Power Spectral Density (PSD) function. Both are second-moment measures of the time series, however, it is only possible to translate unambiguously from the PSD to the AVAR/HVAR, not vice versa, except in the case of a single noise type, which is severely limiting in real-life applications. This note elaborates an analytical method to generate an approximated PSD expressed as a set of power-laws defined in specific intervals in the frequency domain, starting from an AVAR/HVAR expressed a set of power-laws in the time domain. The proposed algorithm is straightforward to implement, applicable to all noise types (and combinations thereof) and can be self-validated by reconstructing the corresponding AVAR/HVAR by direct calculus. We also report on its limitations of and analytical expressions of the continuous version of this algorithm. Coupling with well-established algorithms relying on the PSD for power-law noise generation, the ensuing method encompasses the capability for generating multi-colored noise in end-to-end simulations, as demonstrated hereby for NASA's Deep Space Atomic Clock.