Time-frequency Image Processing Research Articles

Micromotion Parameter Estimation of Free Rigid Targets Based on Radar Micro-Doppler

In this paper, an estimation method of micromotion parameters for free rigid targets using micro-Doppler (mD) features is investigated. These parameters include spin rate, precession rate, nutation angle, and inertia ratio. They represent the microdynamic characteristics and intrinsic properties of targets. The time variation of mD frequency is found complicated yet valuable to estimate the micromotion parameters. From the viewpoint of the spectra of mixed mD time-frequency (TF) data sequences, the theoretical analysis and mathematical derivation are conducted in detail according to the scatterer distribution of rigid bodies. We then present an approach to realize the micromotion parameter estimation from radar mD echoes. It mainly consists of TF transform, TF image processing, mixed mD TF data sequence formation, and spectral estimation. Simulation experiments and result discussion are carried out to demonstrate the effectiveness of the proposed estimation method.

A methodology for time-frequency image processing applied to the classification of non-stationary multichannel signals using instantaneous frequency descriptors with application to newborn EEG signals

Abstract This article presents a general methodology for processing non-stationary signals for the purpose of classification and localization. The methodology combines methods adapted from three complementary areas: time-frequency signal analysis, multichannel signal analysis and image processing. The latter three combine in a new methodology referred to as multichannel time-frequency image processing which is applied to the problem of classifying electroencephalogram (EEG) abnormalities in both adults and newborns. A combination of signal related features and image related features are used by merging key instantaneous frequency descriptors which characterize the signal non-stationarities. The results obtained show that, firstly, the features based on time-frequency image processing techniques such as image segmentation, improve the performance of EEG abnormalities detection in the classification systems based on multi-SVM and neural network classifiers. Secondly, these discriminating features are able to better detect the correlation between newborn EEG signals in a multichannel-based newborn EEG seizure detection for the purpose of localizing EEG abnormalities on the scalp.