A blind signal detection algorithm for passive location system based on troposcatter

2018 ◽  
Vol 10 (10) ◽  
pp. 1128-1133
Author(s):  
Zan Liu ◽  
Xihong Chen ◽  
Qiang Liu ◽  
Zedong Xie
Keyword(s):  
Signal Detection ◽  
Energy Detection ◽  
Detection Algorithm ◽  
Ensemble Empirical Mode Decomposition ◽  
Passive Location ◽  
Intrinsic Mode Functions ◽  
Location System ◽  
Blind Signal ◽  
Mode Decomposition ◽  
Signal Simulation

AbstractTo improve detection performance of passive location system based on troposcatter, we propose a blind signal detection algorithm. According to our algorithm, complementary ensemble empirical mode decomposition decomposes the received signal into several intrinsic mode functions (IMFs). To reconstruct the signal and background noises, difference between the entropy of adjacent IMFs is utilized as a standard. Different IMFs are utilized to estimate threshold of energy detection algorithm and energy level of received signal. Simulation examples indicate that the proposed algorithm can blindly and effectively detect the signal.

scholarly journals A Novel Signal Separation and De-Noising Technique for Doppler Radar Vital Signal Detection

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4751 ◽  
Author(s):  
Xiaoling Li ◽  
Bin Liu ◽  
Yang Liu ◽  
Jiawei Li ◽  
Jiarui Lai ◽  
...  
Keyword(s):  
Signal Detection ◽  
Spectrum Analysis ◽  
Doppler Radar ◽  
Ensemble Empirical Mode Decomposition ◽  
Sample Entropy ◽  
Signal Separation ◽  
Radar Signal ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Adaptive Noise

Doppler radar for monitoring vital signals is an emerging tool, and how to remove the noise during the detection process and reconstruct the accurate respiration and heartbeat signals are hot issues in current research. In this paper, a novel radar vital signal separation and de-noising technique based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sample entropy (SampEn), and wavelet threshold is proposed. First, the noisy radar signal was decomposed into a series of intrinsic mode functions (IMFs) using ICEEMDAN. Then, each IMF was analyzed using SampEn to find out the first few IMFs containing noise, and these IMFs were de-noised using the wavelet threshold. Finally, in order to extract accurate vital signals, spectrum analysis and Kullback–Leible (KL) divergence calculations were performed on all IMFs, and appropriate IMFs were selected to reconstruct respiration and heartbeat signals. Moreover, as far as we know, there is almost no previous research on radar vital signal de-noising based on the proposed technique. The effectiveness of the algorithm was verified using simulated and measured experiments. The results show that the proposed algorithm could effectively reduce the noise and was superior to the existing de-noising technologies, which is beneficial for extracting more accurate vital signals.

scholarly journals Faulty Line Selection Based on Modified CEEMDAN Optimal Denoising Smooth Model and Duffing Oscillator for Un-Effectively Grounded System

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Sizu Hou ◽  
Wei Guo
Keyword(s):  
Signal Detection ◽  
Empirical Mode Decomposition ◽  
Duffing Oscillator ◽  
Ensemble Empirical Mode Decomposition ◽  
Original Signal ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Smooth Model ◽  
Zero Sequence ◽  
Line Selection

As the un-effectively grounded system fails, the zero-sequence current contains strong noise and nonstationary features. This paper proposes a novel faulty line selection method based on modified complete ensemble empirical mode decomposition with adaptive noise (MCEEMDAN) and Duffing oscillator. Here, based on multiscale permutation entropy, fuzzy c-means clustering, and general regression neural network for abnormal signal detection, the MCEEMDAN is proposed. The endpoint mirror method is used to suppress the endpoint effect problem in the decomposition stage. The proposed algorithm is able to decompose the original signal into a series of intrinsic mode functions, which can complete the first filtering. The research shows that it can efficiently suppress the mode confusing phenomenon of empirical mode decomposition (EMD) and is also more complete and orthogonal than ensemble empirical mode decomposition (EEMD) and complementary ensemble empirical mode decomposition (CEEMD). The optimal denoising smooth model is established for choosing optimal intrinsic mode functions to complete the second filtering. It can ensure that the reconstructed filtered signal has better smoothness and similarity. The optimal denoising smooth model of MCEEMDAN can not only keep useful details of the original signal but also reduce the noise and smooth signal. The bifurcation characteristic of the chaotic oscillator is applied in weak signal detection. The zero-sequence current’s denoising result is extracted as the input signal of the Duffing system. The faulty line could be selected by observing the phase diagram of the system. The research results verify the usability and effectiveness of the proposed method.

Ensemble Empirical Mode Decomposition Based Deep Learning Model for Short-Term Wind Power Forecasting

2022 ◽  
Author(s):  
J.M. González-Sopeña
Keyword(s):  
Deep Learning ◽  
Wind Power ◽  
Empirical Mode Decomposition ◽  
Time Series Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Series Data ◽  
Intrinsic Mode Functions ◽  
Wind Power Forecasting ◽  
Mode Decomposition ◽  
Power Forecasting

Abstract. In the last few years, wind power forecasting has established itself as an essential tool in the energy industry due to the increase of wind power penetration in the electric grid. This paper presents a wind power forecasting method based on ensemble empirical mode decomposition (EEMD) and deep learning. EEMD is employed to decompose wind power time series data into several intrinsic mode functions and a residual component. Afterwards, every intrinsic mode function is trained by means of a CNN-LSTM architecture. Finally, wind power forecast is obtained by adding the prediction of every component. Compared to the benchmark model, the proposed approach provides more accurate predictions for several time horizons. Furthermore, prediction intervals are modelled using quantile regression.

scholarly journals Fault Diagnosis for Analog Circuits by Using EEMD, Relative Entropy, and ELM

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Jian Xiong ◽  
Shulin Tian ◽  
Chenglin Yang
Keyword(s):  
Fault Diagnosis ◽  
Relative Entropy ◽  
Analog Circuits ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Eemd Method ◽  
Learning Machine ◽  
Elm Classifier

This paper presents a novel fault diagnosis method for analog circuits using ensemble empirical mode decomposition (EEMD), relative entropy, and extreme learning machine (ELM). First, nominal and faulty response waveforms of a circuit are measured, respectively, and then are decomposed into intrinsic mode functions (IMFs) with the EEMD method. Second, through comparing the nominal IMFs with the faulty IMFs, kurtosis and relative entropy are calculated for each IMF. Next, a feature vector is obtained for each faulty circuit. Finally, an ELM classifier is trained with these feature vectors for fault diagnosis. Via validating with two benchmark circuits, results show that the proposed method is applicable for analog fault diagnosis with acceptable levels of accuracy and time cost.

Spatiotemporal Trend and Variability of Precipitation in Taiwan Based on Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD)

2021 ◽  
Author(s):  
Chun-Hsiang Tang ◽  
Christina W. Tsai
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Short Term ◽  
Time Frequency ◽  
Spatial Locality ◽  
Temperature And Precipitation ◽  
Mode Decomposition ◽  
Climatic Scenarios

<p>Abstract</p><p>Most of the time series in nature are nonlinear and nonstationary affected by climate change particularly. It is inevitable that Taiwan has also experienced frequent drought events in recent years. However, drought events are natural disasters with no clear warnings and their influences are cumulative. The difficulty of detecting and analyzing the drought phenomenon remains. To deal with the above-mentioned problem, Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD) is introduced to analyze the temperature and rainfall data from 1975~2018 in this study, which is a powerful method developed for the time-frequency analysis of nonlinear, nonstationary time series. This method can not only analyze the spatial locality and temporal locality of signals but also decompose the multiple-dimensional time series into several Intrinsic Mode Functions (IMFs). By the set of IMFs, the meaningful instantaneous frequency and the trend of the signals can be observed. Considering stochastic and deterministic influences, to enhance the accuracy this study also reconstruct IMFs into two components, stochastic and deterministic, by the coefficient of auto-correlation.</p><p>In this study, the influences of temperature and precipitation on the drought events will be discussed. Furthermore, to decrease the significant impact of drought events, this study also attempts to forecast the occurrences of drought events in the short-term via the Artificial Neural Network technique. And, based on the CMIP5 model, this study also investigates the trend and variability of drought events and warming in different climatic scenarios.</p><p> </p><p>Keywords: Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD), Intrinsic Mode Function(IMF), Drought</p>

scholarly journals A Blind Signal Samples Detection Algorithm for Accurate Primary User Traffic Estimation

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4136
Author(s):  
Jakub Nikonowicz ◽  
Aamir Mahmood ◽  
Mikael Gidlund
Keyword(s):  
Signal To Noise Ratio ◽  
Energy Detection ◽  
Low Complexity ◽  
Primary User ◽  
Time Frame ◽  
Detection Algorithm ◽  
Experimental Simulation ◽  
Spectrum Access ◽  
Blind Signal ◽  
Occupancy Estimation

The energy detection process for enabling opportunistic spectrum access in dynamic primary user (PU) scenarios, where PU changes state from active to inactive at random time instances, requires the estimation of several parameters ranging from noise variance and signal-to-noise ratio (SNR) to instantaneous and average PU activity. A prerequisite to parameter estimation is an accurate extraction of the signal and noise samples in a received signal time frame. In this paper, we propose a low-complexity and accurate signal samples detection algorithm as compared to well-known methods, which is also blind to the PU activity distribution. The proposed algorithm is analyzed in a semi-experimental simulation setup for its accuracy and time complexity in recognizing signal and noise samples, and its use in channel occupancy estimation, under varying occupancy and SNR of the PU signal. The results confirm its suitability for acquiring the necessary information on the dynamic behavior of PU, which is otherwise assumed to be known in the literature.

scholarly journals Gearbox Fault Diagnosis Using Complementary Ensemble Empirical Mode Decomposition and Permutation Entropy

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Liye Zhao ◽  
Wei Yu ◽  
Ruqiang Yan
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Correlation Coefficients ◽  
Ensemble Empirical Mode Decomposition ◽  
Permutation Entropy ◽  
Support Vector ◽  
Svm Classifier ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition

This paper presents an improved gearbox fault diagnosis approach by integrating complementary ensemble empirical mode decomposition (CEEMD) with permutation entropy (PE). The presented approach identifies faults appearing in a gearbox system based on PE values calculated from selected intrinsic mode functions (IMFs) of vibration signals decomposed by CEEMD. Specifically, CEEMD is first used to decompose vibration signals characterizing various defect severities into a series of IMFs. Then, filtered vibration signals are obtained from appropriate selection of IMFs, and correlation coefficients between the filtered signal and each IMF are used as the basis for useful IMFs selection. Subsequently, PE values of those selected IMFs are utilized as input features to a support vector machine (SVM) classifier for characterizing the defect severity of a gearbox. Case study conducted on a gearbox system indicates the effectiveness of the proposed approach for identifying the gearbox faults.

A Modified EEMD Decomposition for the Detection of Rolling Bearing Faults

2014 ◽  
Vol 548-549 ◽  
pp. 369-373
Author(s):  
Yuan Cheng Shi ◽  
Yong Ying Jiang ◽  
Hai Feng Gao ◽  
Jia Wei Xiang
Keyword(s):  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Hilbert Spectrum ◽  
Bearing Faults ◽  
Mode Decomposition ◽  
Fault Features ◽  
Rolling Element ◽  
Inner Race

The vibration signals of rolling element bearings are non-linear and non-stationary and the corresponding fault features are difficult to be extracted. EEMD (Ensemble empirical mode decomposition) is effective to detect bearing faults. In the present investigation, MEEMD (Modified EEMD) is presented to diagnose the outer and inner race faults of bearings. The original vibration signals are analyzed using IMFs (intrinsic mode functions) extracted by MEEMD decomposition and Hilbert spectrum in the proposed method. The numerical and experimental results of the comparison between MEEMD and EEMD indicate that the proposed method is more effective to extract the fault features of outer and inner race of bearings than EEMD.

Adaptive Ultrasound Tissue Harmonic Imaging Based on an Improved Ensemble Empirical Mode Decomposition Algorithm

2020 ◽  
Vol 42 (2) ◽  
pp. 57-73
Author(s):  
Suya Han ◽  
Yufeng Zhang ◽  
Keyan Wu ◽  
Bingbing He ◽  
Kexin Zhang ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Second Harmonic ◽  
Ensemble Empirical Mode Decomposition ◽  
Harmonic Imaging ◽  
Motion Artifacts ◽  
Intrinsic Mode Functions ◽  
Tissue Harmonic Imaging ◽  
Mode Decomposition ◽  
Adaptive Noise ◽  
Harmonic Components

Complete and accurate separation of harmonic components from the ultrasonic radio frequency (RF) echo signals is essential to improve the quality of harmonic imaging. There are limitations in the existing two commonly used separation methods, that is, the subjectivity for the high-pass filtering (S_HPF) method and motion artifacts for the pulse inversion (S_PI) method. A novel separation method called S_CEEMDAN, based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm, is proposed to adaptively separate the second harmonic components for ultrasound tissue harmonic imaging. First, the ensemble size of the CEEMDAN algorithm is calculated adaptively according to the standard deviation of the added white noise. A set of intrinsic mode functions (IMFs) is then obtained by the CEEMDAN algorithm from the ultrasonic RF echo signals. According to the IMF spectra, the IMFs that contain both fundamental and harmonic components are further decomposed. The separation process is performed until all the obtained IMFs have been divided into either fundamental or harmonic categories. Finally, the fundamental and harmonic RF echo signals are obtained from the accumulations of signals from these two categories, respectively. In simulation experiments based on CREANUIS, the S_CEEMDAN-based results are similar to the S_HPF-based results, but better than the S_PI-based results. For the dynamic carotid artery measurements, the contrasts, contrast-to-noise ratios (CNRs), and tissue-to-clutter ratios (TCRs) of the harmonic images based on the S_CEEMDAN are averagely increased by 31.43% and 50.82%, 18.96% and 10.83%, as well as 34.23% and 44.18%, respectively, compared with those based on the S_HPF and S_PI methods. In conclusion, the S_CEEMDAN method provides improved harmonic images owing to its good adaptivity and lower motion artifacts, and is thus a potential alternative to the current methods for ultrasonic harmonic imaging.

scholarly journals Biometric Identification Method for Heart Sound Based on Multimodal Multiscale Dispersion Entropy

Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 238 ◽  
Author(s):  
Xiefeng Cheng ◽  
Pengfei Wang ◽  
Chenjun She
Keyword(s):  
Markov Models ◽  
Heart Sound ◽  
Recognition Rate ◽  
Ensemble Empirical Mode Decomposition ◽  
Heart Sounds ◽  
Feature Representation ◽  
Intrinsic Mode Functions ◽  
Single Cycle ◽  
Starting Position ◽  
Mode Decomposition

In this paper, a new method of biometric characterization of heart sounds based on multimodal multiscale dispersion entropy is proposed. Firstly, the heart sound is periodically segmented, and then each single-cycle heart sound is decomposed into a group of intrinsic mode functions (IMFs) by improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). These IMFs are then segmented to a series of frames, which is used to calculate the refine composite multiscale dispersion entropy (RCMDE) as the characteristic representation of heart sound. In the simulation experiments I, carried out on the open heart sounds database Michigan, Washington and Littman, the feature representation method was combined with the heart sound segmentation method based on logistic regression (LR) and hidden semi-Markov models (HSMM), and feature selection was performed through the Fisher ratio (FR). Finally, the Euclidean distance (ED) and the close principle are used for matching and identification, and the recognition accuracy rate was 96.08%. To improve the practical application value of this method, the proposed method was applied to 80 heart sounds database constructed by 40 volunteer heart sounds to discuss the effect of single-cycle heart sounds with different starting positions on performance in experiment II. The experimental results show that the single-cycle heart sound with the starting position of the start of the first heart sound (S1) has the highest recognition rate of 97.5%. In summary, the proposed method is effective for heart sound biometric recognition.

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