Improved Instantaneous Frequency Estimation of Multi-Component FM Signals

In this paper, we address the problem of instantaneous frequency (IF) estimation of non-linear, multi-component frequency modulated (FM) signals in the presence of burst missing data samples, where different signal components have distinct amplitude levels. Burst missing data induce significant artifacts in the time-frequency (TF) representations of such signals, thus making identification of true IFs challenging. We propose a technique that involves local peak detection and filtering within a window at each time instant. The threshold for each local TF segment is adapted based on the local maximum values of the signal within the segment. The proposed approach not only mitigates the undesired impacts of the artifacts and cross-terms due to burst missing data samples, but also successfully resolves signal components with distinct amplitude levels and preserves a high resolution of the auto-terms. The effectiveness of the proposed method and its superiority over existing techniques are verified through simulation results.