Time–frequency analysis

About: Time–frequency analysis is a research topic. Over the lifetime, 5407 publications have been published within this topic receiving 104346 citations.

Deep STFT-CNN for Spectrum Sensing in Cognitive Radio

Abstract: Spectrum sensing is one of the crucial technologies used to solve the shortage of spectrum resources. In this letter, based on the short-time Fourier transform (STFT) and convolutional neural network (CNN), we firstly develop a STFT-CNN method for spectrum sensing. The proposed method exploits the time-frequency domain information of the signal samples and achieves the state of the art detection performance. In particular, the method is suitable for various primary users’ signals and does not need any priori information. Besides, we also analyze the signal-to-noise ratio robustness and the generalization ability of the proposed algorithm. Finally, simulation results demonstrate that the proposed method outperforms other popular spectrum sensing methods. Notably, the proposed method can achieve a detection probability of 90.2% with a false alarm probability of 10% at SNR = −15dB.

Adaptive time-frequency representations for multiple structures

Abstract: We propose an adaptive quadratic time-frequency representation (QTFR) based on a matching pursuit signal decomposition that uses a dictionary with elements matched to the instantaneous frequency of the analysis signal components. We form the QTFR as a weighted linear superposition of QTFRs chosen by the algorithm to provide a highly localized representation for each of the adaptively selected dictionary elements. This is advantageous as the resulting representations are parsimonious and reduce the effect of cross terms. Also, they exhibit maximum time-frequency localization for the difficult analysis case of signals with multiple components that have different time-frequency characteristics. Thus, the new technique can be used to analyze and classify multi-structure signal components as demonstrated by our synthetic and real data simulation examples.

Multi-resolution time-frequency analysis for detection of rhythms of EEG signals

Abstract: In recent years, various time-frequency methods have been applied widely For detecting all kinds of feature waves and abnormal waves in EEG signals. But because of their nature and some inherent limitations, their application in EEG analysis has been limited. Considering the excellence and shortcomings of STFT (short time Fourier transform) and wavelets, in the "virtual EEG recording and analysis instrumentation", the multi-resolution time-frequency analysis method, based on STFT and wavelet packet transform, has been introduced to advance the self-adaptive ability for signals, so more flexible division of frequency bands in EEG can be obtained and the basic rhythms in EEG signals can be detected efficiently.

Radar emitter recognition based on the short time fourier transform and convolutional neural networks

Abstract: To improve the recognition rate of radar emitters with complex signal system in an awful electromagnetic environment, a new recognition method based on short time Fourier transform (STFT) and convolutional neural networks (CNN) was proposed. In this method, STFT obtains the time-frequency distribution of radar emitter in-pulse modulated signals and CNN extracted the features of different radar signals with the processed data. Before the time-frequency distribution (TFD) arrays were input to the CNN, a base noise reduction was conducted after six-time zero-means scaling. In the end of the classification, an additional operation was made to distinguish NS and BPSK. Simulations were implemented to present the high recognition performance of the method.