IF estimation for multicomponent signals using image processing techniques in the time–frequency domain

• Published in: Signal processing Vol. 87; no. 6; pp. 1234 - 1250
• Main Authors: Rankine, L., Mesbah, M., Boashash, B.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.06.2007; Elsevier Science
• Subjects: Applied sciences; Biological and medical sciences; Computerized, statistical medical data processing and models in biomedicine; Detection, estimation, filtering, equalization, prediction; EEG; Exact sciences and technology; Image processing; Information, signal and communications theory; Instantaneous frequency; Medical management aid. Diagnosis aid; Medical sciences; Miscellaneous; Multicomponent signals; Signal and communications theory; Signal processing; Signal, noise; Telecommunications and information theory; Time–frequency representation; Time–frequency representation; Instantaneous frequency; Multicomponent signals; Image processing; EEG; Time-frequency analysis; Filtering; Time frequency domain method; Electroencephalography; Two dimensional model; Complex signal; Time-frequency representation; Implementation; Peak detectors; Least squares method; Signal processing; Curve fitting; Signal to noise ratio
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2006.10.013

This paper presents a method for estimating the instantaneous frequency (IF) of multicomponent signals. The technique involves, firstly, the transformation of the one-dimensional signal to the two-dimensional time–frequency (TF) domain using a reduced interference quadratic TF distribution. IF estimation of signal components is then achieved by implementing two image processing steps: local peak detection of the TF representation followed by an image processing technique called component linking. The proposed IF estimator is tested on noisy synthetic monocomponent and multicomponent signals exhibiting linear and nonlinear laws. For low signal-to-noise ratio (SNR) environments, a TF peak filtering preprocessing step is used for signal enhancement. Application of the IF estimation scheme to real signals is illustrated with newborn EEG signals. Finally, to illustrate the potential use of the proposed IF estimation method in classifying signals based on their TF components’ IFs, a classification method using least squares data-fitting is proposed and illustrated on synthetic and real signals.