Iterative missing data recovery algorithm for non-stationary signals

This paper proposes an iterative algorithm to reconstruct missing samples from non-stationary signals. The proposed algorithm is based on the well-known amplitude-modulation frequency-modulation model for non-stationary signals. The method initially estimates the instantaneous frequencies of the observed multi-component signal. The estimated IFs are then used to de-chirp the corresponding components to convert them into stationary components. Following this, a relatively recent nonparametric iterative missing data recovery procedure is employed to reconstruct the time-varying amplitudes of the signal components. The complete signal is constructed by adding all the estimated components, which is used as an input signal to re-estimate the IFs and time-varying amplitudes in an iterative procedure. Studies based on simulated and real data sets show that the proposed approach provides better estimates as compared to the state of the art.

Automated summary

This paper proposes an algorithm to reconstruct missing samples from non-stationary signals. The algorithm estimates the instantaneous frequencies and time-varying amplitudes to recover the complete signal. Research results show that this method provides more accurate estimates compared to existing techniques.