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.

[1]  Yimin Zhang,et al.  Continuous structure based Bayesian compressive sensing for sparse reconstruction of time-frequency distributions , 2014, 2014 19th International Conference on Digital Signal Processing.

[2]  Jian Li,et al.  Source Localization and Sensing: A Nonparametric Iterative Adaptive Approach Based on Weighted Least Squares , 2010, IEEE Transactions on Aerospace and Electronic Systems.

[3]  Branka Jokanovic,et al.  Time-frequency kernel design for sparse joint-variable signal representations , 2014, 2014 22nd European Signal Processing Conference (EUSIPCO).

[4]  Boualem Boashash,et al.  Estimating and interpreting the instantaneous frequency of a signal. I. Fundamentals , 1992, Proc. IEEE.

[5]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[6]  P. Stoica,et al.  High-resolution SAR imaging with angular diversity , 2001 .

[7]  Braham Himed,et al.  Sparsity-based time-frequency representation of FM signals with burst missing samples , 2019, Signal Process..

[8]  Nabeel Ali Khan,et al.  Multi‐component instantaneous frequency estimation using locally adaptive directional time frequency distributions , 2016 .

[9]  Leon Cohen,et al.  Time Frequency Analysis: Theory and Applications , 1994 .

[10]  Branka Jokanovic,et al.  Reduced Interference Sparse Time-Frequency Distributions for Compressed Observations , 2015, IEEE Transactions on Signal Processing.

[11]  Branka Jokanovic,et al.  A sparsity-perspective to quadratic time-frequency distributions , 2015, Digit. Signal Process..

[12]  Douglas L. Jones,et al.  An adaptive optimal-kernel time-frequency representation , 1995, IEEE Trans. Signal Process..

[13]  Erik G. Larsson,et al.  Adaptive Filter-bank Approach to Restoration and Spectral Analysis of Gapped Data* , 2000 .

[14]  Jian Li,et al.  Spectral analysis of periodically gapped data , 2003 .

[15]  Yimin D. Zhang.  Resilient quadratic time-frequency distribution for FM signals with gapped missing data , 2017, 2017 IEEE Radar Conference (RadarConf).

[16]  Braham Himed,et al.  Reduced interference time-frequency representations and sparse reconstruction of undersampled data , 2013, 21st European Signal Processing Conference (EUSIPCO 2013).

[17]  L. Cohen,et al.  Time-frequency distributions-a review , 1989, Proc. IEEE.

[18]  Hao Ling,et al.  Time-Frequency Transforms for Radar Imaging and Signal Analysis , 2002 .

[19]  Yimin D. Zhang,et al.  Robust Time–Frequency Analysis of Multiple FM Signals With Burst Missing Samples , 2019, IEEE Signal Processing Letters.

[20]  Boualem Boashash,et al.  Estimating and interpreting the instantaneous frequency of a signal. II. A/lgorithms and applications , 1992, Proc. IEEE.

[21]  Braham Himed,et al.  Improved Instantaneous Frequency Estimation of Multi-Component FM Signals , 2019, 2019 IEEE Radar Conference (RadarConf).

[22]  Jian Li,et al.  Missing Data Recovery Via a Nonparametric Iterative Adaptive Approach , 2009, IEEE Signal Processing Letters.

[23]  Braham Himed,et al.  Sequential Time-Frequency Signature Estimation of Multi-Component FM Signals , 2019, 2019 53rd Asilomar Conference on Signals, Systems, and Computers.