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.

Broadband nonstationary interference excision for spread spectrum communications using time-frequency synthesis

Abstract: A new method is introduced for interference excision in spread spectrum communications. Time-frequency synthesis techniques are used to synthesize the nonstationary jammer from the time-frequency domain using least-squares methods. The synthesized jammer is then subtracted from the incoming data in the time domain, leading to increased signal to interference ratio at the input of the correlator. The paper focuses on jammers with constant modulus where the jamming signal is a polynomial phase. With this a priori knowledge, the jammer signal amplitude is restored by projecting each sample of the synthesized signal to a circle representing its constant modulus. With the phase matching provided by the least-squares synthesis method and amplitude matching underlying the projection operation, the paper shows a significant improvement in receiver performance/bit error rates over the case where no projection is performed.

Convergence of an iterative time-variant filtering based on discrete Gabor transform

Abstract: An iterative time-variant filtering based on the discrete Gabor transform (DGT) has been previously proposed by the authors. In this article, we present a proof of the convergence of the iterative algorithm under a sufficient condition on the analysis and synthesis window functions of the DGT. In the meantime, we show that the iterative algorithm refines the least squares solution.

A parameter extraction technique for FMCW radar signals using Wigner-Hough-Radon transform

Abstract: An autonomous parameter extraction algorithm for frequency modulated continuous wave (FMCW) radar signals using Wigner-Ville Distribution (WVD)-Hough transform was investigated in [1] and extraction of polyphase radar modulation parameters using a Wigner-Ville distribution-Radon transform was investigated in [2]. The algorithm in [1] produced very dependable results with as low as −6 dB SNR levels, however some degradation has been observed below −6 dB SNR. The proposed approach in this study uses the WVD as a time-frequency (T-F) detection technique and combined Hough-Radon transform (HRT) to identify the parameters of the modulation. We showed that our algorithm can extract FMCW radar modulation parameters at low SNR levels, such as −9 dB, efficiently.

Time–frequency modelling and discrimination of noise in the electrocardiogram

Abstract: In widely spread home care applications of ECG recorders, the traditional approach to the problem of noise immunity is no longer sufficient. This paper presents a new ECG-dedicated noise removal technique based on a time–frequency noise model computed in a quasi-continuous way. Our algorithm makes use of the local bandwidth variability of cardiac electrical representation and splits the discrete time sequence into two sub-planes. The background activities of any origin (muscle, power line interference, etc) are measured in the regions of the time–frequency plane, situated above the local bandwidth of the signal. The noise estimate on each particular scale is non-uniformly sampled and needs to be extrapolated to the regions where the components of cardiac representation are normally expected. On the lower scales, the noise contribution is computed with the use of square polynomial extrapolation. The time–frequency representation of noise, partially measured and partially calculated, is arithmetically subtracted from the noisy signal, and the inverse time–frequency transform yields a noise-free cardiac representation. The algorithm was tested with the use of CSE database records with the addition of MIT-BIH database noise patterns. The static and dynamic performance of the algorithm is sufficient to ameliorate the signal-to-noise ratio by more than 11 dB.

Bearing fault diagnosis using Wavelet analysis

Abstract: One-dimensional discrete wavelet transform is used to process the bearing fault signal in this paper. Firstly, the bearing fault data is decomposed to multi-layer. Then the fault feature signal is reconstructed. In order to detect the bearing failure and determine the area of it, the reconstructed signal is processed by the Hilbert transform demodulation and spectrum refining. The results show that the frequency of failure point matches well with theoretical one using this method. This method is simple and reliable and thus provides a scientific method for early warning and exclusion of failure.