Determining joint periodicities in multi-time data with sampling uncertainties

In this work, we introduce a novel approach for determining joint sparse spectrum from several non-uniformly sampled data sets, where each set is assumed to have its own, possibly disjoint, and only partially known, sampling times. The potential of the proposed illustrated using spectral estimation problem in paleoclimatology. problem, point derives separate ice core measurement, resulting that even though all measurements reflect same periodicities, times phases differ among sets. addition, are approximately known. estimate exploiting available formulated reconstruction framework allowing reliable robust underlying periodicities. corresponding misspecified Cramér-Rao lower bound, accounting expected uncertainties, derived method shown attain bound when signal noise ratio sufficiently high. performance as compared other commonly used approaches both simulated measured