A novel weak signal detection method for Linear Frequency Modulation signal based on bistable system and Fractional Fourier Transform

Zhao, Dazhi; Luo, Maokang

doi:10.1016/j.ijleo.2016.01.057

Cited by 9 publications

(1 citation statement)

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“…Significant research has been carried out into the recovery of these signals [13][14][15], and it has been found that the main way to improve the signal-to-noise ratio (SNR) is to either eliminate or suppress the noise. Recently, wavelet transforms [16], adaptive filtering [17], empirical mode decomposition (EMD) [18], and local mean decomposition [19] have been successfully applied in signal denoising [20]. For example, in the denoising method based on wavelet transforms, different wavelet bases have different frequency characteristics.…”

Section: Introductionmentioning

confidence: 99%

Fast recovery of weak signal based on three‐dimensional curvelet transform and generalised cross validation

Wen

Xie

et al. 2018

IET signal process.

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In order to recover weak signal that is submerged in heavy background noise, a weak signal recovery method based on three-dimensional curvelet transform and generalised cross validation (GCV) is presented. Their method includes three stages: firstly, three-dimensional dataset was decomposed into sub-volumes by three-dimensional curvelet transform, and then Graphics Processing Unit (GPU) is used to improve the speed of parallel processing of sub-volumes. Secondly, the GCV criterion and genetic algorithm were used to improve the signal-to-noise ratio (SNR) of the processed signal. Finally, according to different distribution between the effective signal energy and the noise energy, adaptive filter is used to enhance the recovered weak signal. Furthermore, to verify the availability of the method, a wedge of simulation data and 100 groups of three-dimensional seismic data for testing are analysed, the stratigraphic structure information is much clearer in the processed seismic data than in the original data. The results show that the SNR of the recovered data is improved by 3 dB and the band of frequency is increased by 100 Hz. Their method is four to five times faster than a recovery method based on CPU processing.

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