Abstract
To solve the problem of poor recognition effect of transient signal in low Signal-to-Noise ratio (SNR) and strong interference electromagnetic environment, a morphological filtering method based on the multi-scale combined difference product (MCDPMF) was proposed. This paper concentrates on the issues of sudden changes in transient electronic signal, such as impulses and edges. Firstly, it provides a difference product morphological filter. Moreover, the extended and multi-origin morphological Structural Elements (<i>SEs</i>) is constructed, combining with the multi-structural layers <inline-formula> <tex-math notation="LaTeX">${a}$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">${a}$ </tex-math></inline-formula> indicates the structural layers of the MCDPMF), the transient electromagnetic weak signal is multi-scale filtered. They are used to optimize the number of the structure layers <inline-formula> <tex-math notation="LaTeX">${a}$ </tex-math></inline-formula> adaptively based on the amplitude characteristic ratio of the positive and negative polarity of the filtered signal (HML value), combining the kurtosis-SNR (<inline-formula> <tex-math notation="LaTeX">$k_{x}-SNR$ </tex-math></inline-formula>) ratio characteristic coefficient. MCDPMF is proposed to enhance the filtering results and suppress the noise frequency points. Meanwhile, it can extract the structure components and identify the features of the transient electromagnetic weak signal. It can be shown from simulation and experimental results that the proposed method is superior to EMD, AVG, OCCO, and other methods in subjective evaluation and objective indicators.
Highlights
W ITH the development of wireless communication, the communication signal system and modulation style are becoming more complex and more diverse
The main contributions are as follows: With the breakthrough of strong noise and abnormal frequency point suppression based on the morphological filtering, the filtering effect under different SNR ratio is discussed by using the extensible, multi-origin, and multi-scale Structural Elements (SEs) and the multi-structural layers a optimized by characteristic parameter index
Synthesize the above discussion, the results show that multi-scale combined difference product morphological filter (MCDPMF) can effectively extract weak transient electromagnetic signals in a strong noise environment
Summary
W ITH the development of wireless communication, the communication signal system and modulation style are becoming more complex and more diverse. The morphological filtering method for transient signal extraction has two problems: (1)Strong background noise environment’s interference components are complex. EDMF[19],DIF (Difference Filter)[34],ACDIF[35] combined with classical morphological operators can alleviate detection error, and has been applied to the device’s fault transient signal reduction These methods cannot evaluate different SNR ratios’ detection effects effectively. The main contributions are as follows: With the breakthrough of strong noise and abnormal frequency point suppression based on the morphological filtering, the filtering effect under different SNR ratio is discussed by using the extensible, multi-origin, and multi-scale SEs and the multi-structural layers a optimized by characteristic parameter index. The method proposed in this paper is feasible and superior in extracting transient electromagnetic weak signals under strong background noise, based on simulation and experimental results
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