scholarly journals Application of Denoising CNN for Noise Suppression and Weak Signal Extraction of Lunar Penetrating Radar Data

2021 ◽  
Vol 13 (4) ◽  
pp. 779
Author(s):  
Haoqiu Zhou ◽  
Xuan Feng ◽  
Zejun Dong ◽  
Cai Liu ◽  
Wenjing Liang
Keyword(s):  
Noise Suppression ◽  
Low Frequency ◽  
Radar Data ◽  
Signal Extraction ◽  
Band Pass Filter ◽  
Weak Signal ◽  
High Quality ◽  
Pass Filter ◽  
Noise Interference ◽  
Mean Filter

As one of the main payloads mounted on the Yutu-2 rover of Chang’E-4 probe, lunar penetrating radar (LPR) aims to map the subsurface structure in the Von Kármán crater. The field LPR data are generally masked by clutters and noises of large quantities. To solve the noise interference, dozens of filtering methods have been applied to LPR data. However, these methods have their limitations, so noise suppression is still a tough issue worth studying. In this article, the denoising convolutional neural network (CNN) framework is applied to the noise suppression and weak signal extraction of 500 MHz LPR data. The results verify that the low-frequency clutters embedded in the LPR data mainly came from the instrument system of the Yutu rover. Besides, compared with the classic band-pass filter and the mean filter, the CNN filter has better performance when dealing with noise interference and weak signal extraction; compared with Kirchhoff migration, it can provide original high-quality radargram with diffraction information. Based on the high-quality radargram provided by the CNN filter, the subsurface sandwich structure is revealed and the weak signals from three sub-layers within the paleo-regolith are extracted.

scholarly journals Application of Mathematical Morphological Filtering to Improve the Resolution of Chang'e-3 Lunar Penetrating Radar Data

2019 ◽  
Vol 11 (5) ◽  
pp. 524 ◽  
Author(s):  
Jianmin Zhang ◽  
Zhaofa Zeng ◽  
Ling Zhang ◽  
Qi Lu ◽  
Kun Wang
Keyword(s):  
High Frequency ◽  
Noise Suppression ◽  
Low Frequency ◽  
Radar Data ◽  
Structural Elements ◽  
Geological Structures ◽  
Mode Decomposition ◽  
Morphological Filtering ◽  
Scale Range ◽  
Band Pass

As one of the important scientific instruments of lunar exploration, the Lunar Penetrating Radar (LPR) onboard China’s Chang'E-3 (CE-3) provides a unique opportunity to image the lunar subsurface structure. Due to the low-frequency and high-frequency noises of the data, only a few geological structures are visible. In order to better improve the resolution of the data, band-pass filtering and empirical mode decomposition filtering (EMD) methods are usually used, but in this paper, we present a mathematical morphological filtering (MMF) method to reduce the noise. The MMF method uses two structural elements with different scales to extract certain scale-range information from the original signal, at the same time, the noise beyond the scale range of the two different structural elements is suppressed. The application on synthetic signals demonstrates that the morphological filtering method has a better performance in noise suppression compared with band-pass filtering and EMD methods. Then, we apply band-pass filtering, EMD, and MMF methods to the LPR data, and the MMF method also achieves a better result. Furthermore, according to the result by MMF method, three stratigraphic zones are revealed along the rover's route.

scholarly journals Application of a New Combination Algorithm in ELF-EM Processing

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 337
Author(s):  
Fukai Li ◽  
Zhiqiang Yang ◽  
Yehuo Fan ◽  
Yuchun Li ◽  
Guang Li
Keyword(s):  
Electromagnetic Wave ◽  
Measurement Data ◽  
Low Frequency ◽  
New Combination ◽  
Frequency Noise ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Band Noise ◽  
Measurement While Drilling ◽  
Field Noise

With regards to the electromagnetic measurement while drilling (EM-MWD), the extremely-low frequency electromagnetic wave signal (ELF-EM) below 20 Hz is usually used as the carrier of downhole measurement data due to the transmission characteristics of the electromagnetic wave (EM). However, influenced by the low frequency noise of drilling, the ELF-EM signal will be inevitably interfered by field noise, which ultimately impedes decoding. The Fourier band-pass filter can effectively remove out-of-band noise but is incapable of handling in-band noise. Therefore, based on the traditional method, a hybrid algorithm of adaptive Wiener algorithm and correlation detection (AWCD) is designed, so as to enhance the in-band noise processing capability, and the effectiveness of such algorithm is well verified through coding and decoding simulation as well as experimental data. The proposed algorithm, as indicated by theoretical analysis and test data, can effectively solve actual engineering issues, providing methodological references to engineers and technicians.

Construction of a High-Pass Digital Filter from a Low-Pass Digital Filter

1994 ◽  
Vol 10 (4) ◽  
pp. 374-381 ◽  
Author(s):  
Stephen D. Murphy ◽  
D. Gordon E. Robertson
Keyword(s):  
Digital Filter ◽  
Low Frequency ◽  
Frequency Noise ◽  
Band Pass Filter ◽  
Low Frequency Noise ◽  
Pass Filter ◽  
Low Pass Filter ◽  
High Pass Filter ◽  
Low Pass ◽  
High Pass

To remove low-frequency noise from data such as DC-bias from electromyo-grams (EMGs) or drift from force transducers, a high-pass filter was constructed from a low-pass filter of known characteristics. A summary of the necessary steps required to transform the low-pass digital were developed. Contaminated EMG and force platform data were used to test the filter. The high-pass filter successfully removed the low-frequency noise from the EMG signals. The high-pass filter was then cascaded with the low-pass filter to produce a band-pass filter to enable simultaneous high- and low-frequency noise reduction.

The Muscle Activity Detection from Surface EMG Signal Using the Morphological Filter

2012 ◽  
Vol 195-196 ◽  
pp. 1137-1141
Author(s):  
Qiang Li ◽  
Bo Li
Keyword(s):  
Muscle Activity ◽  
Interpolation Method ◽  
Surface Emg ◽  
Band Pass Filter ◽  
Morphological Filter ◽  
Activity Detection ◽  
Semg Signal ◽  
Pass Filter ◽  
Noise Interference ◽  
Emg Signal

For the recognition of action sEMG signal, the muscle activity detection is the elementary work, and the morphological filter was explored to achieve the target in this paper. To reduce the noise interference in the collected sEMG signal, the band-pass filter and spectrum interpolation method were applied. Based on two structuring elements, the morphological filter was utilized to separate the action signal from the background signal. Then, the amplitude envelope which could indicate the muscle activity was acquired. The experimental results showed that the satisfying muscle activity detection performance could be implemented by the morphological filter.

Windowed-sinc Band-pass Filter based Reference Signal Extraction for Grid Synchronization

2013 ◽  
Vol 1 (1) ◽  
pp. 23-28 ◽  
Author(s):  
Daming Zhang ◽  
Toan Phung ◽  
John Fletcher ◽  
J. C. Chen ◽  
M. Jiang ◽  
...  
Keyword(s):  
Reference Signal ◽  
Signal Extraction ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Grid Synchronization ◽  
Band Pass
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