engleski

Estimating the Number of Components of a Multicomponent Nonstationary Signal Using the Short-Term Time-Frequency Renyi Entropy

The time-frequency Renyi entropy provides a measure of complexity of a nonstationary multicomponent signal in the time-frequency plane. When the complexity of a signal corresponds to the number of its components, then this information is measured as the Renyi entropy of the time- frequency distribution of the signal. This paper presents a solution to the problem of detecting the number of components that are present in a short time interval of the signal time-frequency distribution, using the short-term Renyi entropy. The method is automatic and it does not require a prior information about the signal. The algorithm is applied on both synthetic and real data, using a quadratic separable kernel time-frequency distribution (TFD). The results confirm that the short-term Renyi entropy can be an effective tool for estimating the local number of components present in the signal. The key aspect of selecting a suitable TFD is also discussed.

Renyi Entropy; Separarable Kernel Time-Frequency Distributions; Components

Special Issue on Advances in Time Frequency and Array Processing of Nonstationary Signals