scholarly journals SAR Target Detection Method Based on Empirical Mode Decomposition

2012 ◽  
Vol 6-7 ◽  
pp. 496-500
Author(s):  
Shi Qi Huang ◽  
Bei He Wang ◽  
Yi Hong Li ◽  
Bei Ge
Keyword(s):  
Signal Processing ◽  
Empirical Mode Decomposition ◽  
Target Detection ◽  
Detection Method ◽  
Detection Algorithm ◽  
Radar Signal ◽  
Sar Images ◽  
Processing Theory ◽  
Mode Decomposition ◽  
Non Stationary Signal

Empirical mode decomposition (EMD) is a new signal processing theory, and it is very much fitting for non-stationary signal processing, such as radar signal. So this paper proposes the new synthetic aperture radar (SAR) image target detection algorithm after analyzing the characteristics of EMD and SAR images. The proposed method performs the EMD operation, feature extraction, election and fusion, which can reduce the affection of speckle. Experimental results show that the proposed method is very effective.

Signal Processing of Motor Imagery EEG Waves Using Empirical Mode Decomposition

2017 ◽  
pp. 197-209
Author(s):  
Ajithkumar Sreekumar ◽  
M. Uttara Kumari ◽  
Krishna Chaithanya Vastare ◽  
Suraj Madenur Sreenivasa ◽  
N. Apoorva
Keyword(s):  
Signal Processing ◽  
Empirical Mode Decomposition ◽  
Motor Imagery ◽  
Mode Decomposition

scholarly journals Radar Signal Emitter Recognition Based on Combined Ensemble Empirical Mode Decomposition and the Generalized S-Transform

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Bin Liu ◽  
Youqian Feng ◽  
Zhonghai Yin ◽  
Xiangyu Fan
Keyword(s):  
Empirical Mode Decomposition ◽  
Prior Information ◽  
Ensemble Empirical Mode Decomposition ◽  
Radar Signal ◽  
Time Frequency ◽  
Low Snr ◽  
Mode Decomposition ◽  
S Transform ◽  
Emitter Recognition ◽  
Generalized S Transform

Present radar signal emitter recognition approaches suffer from a dependency on prior information. Moreover, modern emitter recognition must meet the challenges associated with low probability of intercept technology and other obscuration methodologies based on complex signal modulation and must simultaneously provide a relatively strong ability for extracting weak signals under low SNR values. Therefore, the present article proposes an emitter recognition approach that combines ensemble empirical mode decomposition (EEMD) with the generalized S-transform (GST) for promoting enhanced recognition ability for radar signals with complex modulation under low signal-to-noise ratios in the absence of prior information. The results of Monte Carlo simulations conducted using various mixed signals with additive Gaussian white noise are reported. The results verify that EEMD suppresses the occurrence of mode mixing commonly observed using standard empirical mode decomposition. In addition, EEMD is shown to extract meaningful signal features even under low SNR values, which demonstrates its ability to suppress noise. Finally, EEMD-GST is demonstrated to provide an obviously better time-frequency focusing property than that of either the standard S-transform or the short-time Fourier transform.

scholarly journals Application of variational mode decomposition to seismic random noise reduction

2017 ◽  
Vol 14 (4) ◽  
pp. 888-898 ◽  
Author(s):  
Wei Liu ◽  
Siyuan Cao ◽  
Zhiming Wang
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Random Noise ◽  
Computational Cost ◽  
Superior Performance ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Reconstruction Performance ◽  
Non Stationary Signal

Abstract We have proposed a new denoising method for the simultaneous noise reduction and preservation of seismic signals based on variational mode decomposition (VMD). VMD is a recently developed adaptive signal decomposition method and an advance in non-stationary signal analysis. It solves the mode-mixing and non-optimal reconstruction performance problems of empirical mode decomposition that have existed for a long time. By using VMD, a multi-component signal can be non-recursively decomposed into a series of quasi-orthogonal intrinsic mode functions (IMFs), each of which has a relatively local frequency range. Meanwhile, the signal will focus on a smaller number of obtained IMFs after decomposition, and thus the denoised result is able to be obtained by reconstructing these signal-dominant IMFs. Synthetic examples are given to demonstrate the effectiveness of the proposed approach and comparison is made with the complete ensemble empirical mode decomposition, which demonstrates that the VMD algorithm has lower computational cost and better random noise elimination performance. The application of on field seismic data further illustrates the superior performance of our method in both random noise attenuation and the recovery of seismic events.

Target detection for low cost uncooled MWIR cameras based on empirical mode decomposition

2014 ◽  
Vol 63 ◽  
pp. 222-231 ◽  
Author(s):  
José Piñeiro-Ave ◽  
Manuel Blanco-Velasco ◽  
Fernando Cruz-Roldán ◽  
Antonio Artés-Rodríguez
Keyword(s):  
Empirical Mode Decomposition ◽  
Target Detection ◽  
Low Cost ◽  
Mode Decomposition
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