Improved IF Estimation of Multi-Component FM Signals Through Iterative Adaptive Missing Data Recovery

In this paper, we address a challenging problem of accurate instantaneous frequency (IF) estimation of multicomponent non-linear frequency modulated (FM) signals with distinct amplitude levels in the presence of missing data samples. In such scenarios, it is often difficult to resolve the weaker signal components. Besides, missing data-induced artifacts spread in the time-frequency (TF) domain, further complicating IF estimation. We propose a method that iteratively performs missing data recovery in the time-lag domain based on the least squares criterion in conjunction with signal-adaptive TF kernels. The proposed technique successfully resolves signal components with distinct amplitude levels, preserves a high resolution of the auto-terms and achieves robust TF distributions by mitigating the undesired effects of cross-terms and artifacts due to missing data samples. The effectiveness of the proposed method is verified through various simulation results.