Adaptive modulation interval filtering algorithm based on empirical mode decomposition. (July 2019)

• Record Type: Journal Article
• Authors: Dao, Xinyu; Gao, Min; Li, Chaowang
• Is Part Of: Measurement. Volume 141(2019)
• Page Start: 277
• Page End: 286
• Publication Date: 2019-07
• Subjects: Empirical mode decomposition -- Interval threshold denoising -- Information extraction -- Frequency modulated system; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2019.04.046
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: The energy of white noise and clutter gradually reduce with the order increasing of IMF based on EMD. It is not necessary to decompose the signal after the energy below 10% of the initial energy when adopting EMD de-noising. The adjustment factor can adjust the processing extent according to the extreme and threshold value. Adaptive Modulation Interval Filtering makes the de-noising more flexible. Abstract: To improve the denoising performance of echo signal in a receiver of frequency modulated system, an adaptive modulation interval threshold denoising algorithm on the basis of empirical mode decomposition is proposed. By means of measuring the energy variation in each intrinsic mode function of noise interference and different background clutter, the optimal decomposition stopping time of empirical mode decomposition and the order of intrinsic mode functions needed to be filtered are determined. Combined with the soft interval threshold, an adjustment factor is added into the soft interval threshold formula to process the intrinsic mode function adaptively. The simulation and practical measurement results show that the proposed method improves signal-to-noise ratio by 1–3 dB and reduces the root mean square error by 10–25% compared with the direct empirical mode decomposition denoising method and traditional threshold denoising method.