A joint time-frequency empirical mode decomposition for nonstationary signal separation

Multiple-view time–frequency distribution based on the empirical mode decomposition

“…This means that unrelated signal components are grouped together if they occur at separate times in the signal. A recently proposed modification to the EMD algorithms using the DCT to remove the restriction of continuity in time on an IMF should provide additional improvement in the EMD-TFD [14]. This may help remove interference by components that are separated in time, rather than frequency (the effects of such interference can be seen in Fig.…”

“…The problem with such an implementation is that the EMD does not always accurately decompose multicomponent nonstationary signals, in terms of (1) [14]. Signals that have high bandwidth/duration, 4 IET SIGNAL PROCESSING transients or significant noise result in a decomposition that can be approximated by the output of a bank of stationary low pass filters with dyadically increasing cutoff frequencies, [15].…”

“…Signals that have high bandwidth/duration, 4 IET SIGNAL PROCESSING transients or significant noise result in a decomposition that can be approximated by the output of a bank of stationary low pass filters with dyadically increasing cutoff frequencies, [15]. In addition, the EMD is sensitive to the sampling frequency, and the end conditions of the cubic spline fits [14], [16]. As a result, the EMD can only accurately represent a signal defined with respect to (1) if the signal components are suitably constrained.…”

Fourier‐Bessel transform and time–frequency‐based approach for detecting manoeuvring air target in sea‐clutter

“…Properties of EMD are useful to detect rapid frequency changes [9]. In TF context, (2) can be expressed as [10] x…”

“…This has applications in kernel design for reduced interference TF distributions [10]. EMD operates at the scale of every oscillation, which is extremely advantageous for obtaining more physically meaningful results [11].…”