Analysis of atmospheric radar echoes using Wavelets and EMD

Padmaja, N.; Varadarajan, S.; Kalpana, V.

doi:10.1109/icsss.2013.6623009

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…Interference-frequency identification is observed in the power spectral domain. The time-series radar data that is received is initially allowed for power-spectrum calculation using FFT (Fast Fourier Transform) and Hilbert Huang Transform (HHT) [5][6][7][16]. Mean-noise-level is estimated using the fundamental threshold value for the recognition of interference signals that are similar in nature from every range gate.…”

Section: Interference Detection and Removal Algorithmmentioning

confidence: 99%

“…After Step 2, take away the mean noise level for each and every range bin and plot the corresponding stacked Doppler spectrum [3,4] B. Steps to be followed to perform this Algorithm Using HHT Convert the time series raw signal into spectral signal using HHT [5][6]. The following are the processing steps:…”

Section: A Steps To Be Followed To Perform This Algorithm Using Fftmentioning

confidence: 99%

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Automated Algorithm for Accurate Doppler Profile Detection of MST Radar Signals Considering Removal of Interference

Padmaja¹,

Rani²,

Bharathi³

2019

IJRTE

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 Abstract: Identifying the accurate radar echo returns from the MST (Mesosphere-Stratosphere-Troposphere) region poses serious challenges, as the radar echo signals are of very low amplitude and hidden in noise. The process of extraction of the relevant signal information becomes intricate when the spectra sometimes are infected with interference signals of non-atmospheric origin. An automated algorithm is suggested for eliminating the interference bands in the Doppler spectra of MST radar signals. The process of removal of interference and de-noising of the radar echoes using soft thresholding techniques was achieved simultaneously. The algorithm was practically applied to interference contaminated MST radar data taken from National Atmospheric Research Laboratory (NARL), Gadanki, Tirupati and studied for removal of interference bands. It was found that the projected algorithm effectively removes multiple interference bands and also improves signal detectability. The power and Doppler values (moments) are estimated using FFT and HHT (Hilbert Huang Transform) and median Doppler is plotted on Doppler power spectrum and compared.

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Section: Interference Detection and Removal Algorithmmentioning

confidence: 99%

Section: A Steps To Be Followed To Perform This Algorithm Using Fftmentioning

confidence: 99%

Automated Algorithm for Accurate Doppler Profile Detection of MST Radar Signals Considering Removal of Interference

Padmaja¹,

Rani²,

Bharathi³

2019

IJRTE

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“…Fortunately, empirical mode decomposition (EMD) [ 5 ] can be able to address the is-sues of FT and WT. It has been extensively applied in marine [ 6 ], atmospheric [ 7 , 8 ], and mechanical fault diagnosis [ 9 , 10 , 11 ], etc. Essentially different from FT and WT methods based on a priori assumption of harmonic basis function and wavelet basis function, EMD adaptively decomposes the target signal into several intrinsic mode functions (IMFs) according to the time scale of data itself.…”

Section: Introductionmentioning

confidence: 99%

Optimized Variational Mode Decomposition and Permutation Entropy with Their Application in Feature Extraction of Ship-Radiated Noise

Xie¹,

Hong

Yao³

2021

Entropy

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The complex and changeable marine environment surrounded by a variety of noise, including sounds of marine animals, industrial noise, traffic noise and the noise formed by molecular movement, not only interferes with the normal life of residents near the port, but also exerts a significant influence on feature extraction of ship-radiated noise (S-RN). In this paper, a novel feature extraction technique for S-RN signals based on optimized variational mode decomposition (OVMD), permutation entropy (PE), and normalized Spearman correlation coefficient (NSCC) is proposed. Firstly, with the mode number determined by reverse weighted permutation entropy (RWPE), OVMD decomposes the target signal into a set of intrinsic mode functions (IMFs). The PE of all the IMFs and SCC between each IMF with the raw signal are then calculated, respectively. Subsequently, feature parameters are extracted through the sum of PE weighted by NSCC for the IMFs. Lastly, the obtained feature vectors are input into the support vector machine multi-class classifier (SVM) to discriminate various types of ships. Experimental results indicate that five kinds of S-RN samples can be accurately identified with a recognition rate of 94% by the proposed scheme, which is higher than other previously published methods. Hence, the proposed method is more advantageous in practical applications.

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Feature Extraction Method of Underwater Acoustic Signals Using VMD and Reverse Weighted Permutation Entropy

Gao,

Wu,

Zhu

et al. 2023

2023 6th International Conference on Information Communication and Signal Processing (ICICSP)

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Analysis of atmospheric radar echoes using Wavelets and EMD

Cited by 3 publications

References 11 publications

Automated Algorithm for Accurate Doppler Profile Detection of MST Radar Signals Considering Removal of Interference

Automated Algorithm for Accurate Doppler Profile Detection of MST Radar Signals Considering Removal of Interference

Optimized Variational Mode Decomposition and Permutation Entropy with Their Application in Feature Extraction of Ship-Radiated Noise

Feature Extraction Method of Underwater Acoustic Signals Using VMD and Reverse Weighted Permutation Entropy

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