Empirical Mode Decomposition Filtering Research Articles

An Anti-Different Frequency Interference Algorithm for Environment-Aware Millimeter Wave Radar

An environment-aware millimeter wave radar (EMWR) is one of the most important sensors in an autonomous driving system. With the increasing penetration of an EMWR on an autonomous vehicle, the possibility of radar interference increases accordingly. Interference may seriously affect the detection performance of an EMWR. When the transmitted signals from other EMWRs are received by the EMWR on an autonomous vehicle, the accuracy of target detection will be affected, which may affect the environment-aware performance. Therefore, more and more attention is paid to how to restrain the interference between EMWR signals. Radar interference can be divided into two types: same-frequency interference (SFI) and different-frequency interference (DFI). In this paper, an anti-DFI algorithm of an EMWR is proposed. Firstly, the causes and signal characteristics of DFI are analyzed. Secondly, the signal amplitude is obtained via Hilbert transform to locate the interference according to the signal characteristics. Then, the Lagrange interpolation based on empirical-mode decomposition (EMD) is used to reconstruct the interference signal to reduce the influence of DFI. Finally, the feasibility of the proposed algorithm is verified by the simulation results. The simulation results validate that the interference signal after EMD filtering and Lagrange interpolation can repair the interference region and achieve the purpose of anti-DFI.

Inertial alignment with improved EMD filter of inclinometer in coal drilling rig

Inertial navigation system (INS) is widely used as inclinometer in coal drilling rig to realize navigation and attitude measurement. Vibration of coal drilling rig is one of the significant issues impacting the performance of alignment. Previous work has indicated that part of alignment error caused by vibration of gyroscope can be suppressed by inertial alignment frame. However, vibration error of accelerometer cannot be well suppressed. In this paper, an inertial alignment based on improved empirical mode decomposition (EMD) filter is proposed to address vibration issue in alignment. Moreover, a prediction strategy of extreme endpoints based on relativity of extreme points is proposed to reduce the endpoint error to suppress the accumulative error in EMD filter. The proposed alignment method is applied in laboratory condition. The result shows that the stability of accelerometer improves 42.43% at most, the alignment time reduce from 300 to 100 s and the alignment error of inclination angle is less than 5.4 arcmin. The experiment in coal mining rig shows the alignment time reduces from 300 to 50 s. The proposed alignment method improves the performance of inclinometer effectively and feasibly in alignment period and the improved EMD filter can be also applied in navigation period to suppress the vibration error of accelerometer.

Experiment on a distributed fiber optic interferometric sensing system to monitor and locate urban high-density polyethylene gas pipe leakage

A distributed fiber optical interferometric sensing system was built to monitor and detect urban high-density polyethylene gas pipe leakage. To eliminate the phase drift, the digital phase generated carrier scheme was designed to demodulate the phase difference of interferometers induced by high-density polyethylene gas pipe leakage. Then the empirical mode decomposition and approximate entropy were combined to process fiber optic interferometric signals. Finally, leakage points were located by the null-frequency spectrum after empirical mode decomposition filter denoising. The lab-scale test validated the distributed fiber optical interferometric sensing system leakage location performance and demonstrated a reduced leakage accident missed alarm rate.

Noise suppression in 2D and 3D seismic data with data-driven sifting algorithms

We have developed an empirical-mode decomposition (EMD) algorithm for effective suppression of random and coherent noise in 2D and 3D seismic amplitude data. Unlike other EMD-based methods for seismic data processing, our approach does not involve the time direction in the computation of the signal envelopes needed for the iterative sifting process. Instead, we apply the sifting algorithm spatially in the inline-crossline plane. At each time slice, we calculate the upper and lower signal envelopes by means of a filter whose length is adapted dynamically at each sifting iteration according to the spatial distribution of the extrema. The denoising of a 3D volume is achieved by removing the most oscillating modes of each time slice from the noisy data. We determine the performance of the algorithm by using three public-domain poststack field data sets: one 2D line of the well-known Alaska 2D data set, available from the US Geological Survey; a subset of the Penobscot 3D volume acquired offshore by the Nova Scotia Department of Energy, Canada; and a subset of the Stratton 3D land data from South Texas, available from the Bureau of Economic Geology at the University of Texas at Austin. The results indicate that random and coherent noise, such as footprint signatures, can be mitigated satisfactorily, enhancing the reflectors with negligible signal leakage in most cases. Our method, called empirical-mode filtering (EMF), yields improved results compared to other 2D and 3D techniques, such as [Formula: see text] EMD filter, [Formula: see text] deconvolution, and [Formula: see text]-[Formula: see text]-[Formula: see text] adaptive prediction filtering. EMF exploits the flexibility of EMD on seismic data and is presented as an efficient and easy-to-apply alternative for denoising seismic data with mild to moderate structural complexity.

An Optimized De-Noising Scheme Based on the Null Hypothesis of Intrinsic Mode Functions

Empirical mode decomposition (EMD) offers a data-driven approach for signal filtering. In this letter, a new de-noising scheme is proposed, which estimates a pertinent probability distribution parameter with the Newton-Raphson root-finding method. The parameter estimation reduces the computational cost of relevant mode selection by reducing the range of possible mode distribution shapes. Computational complexity is assessed, revealing significant reduction in the number of operations required in comparison to a previous de-noising scheme which utilized the same form of statistical filtering. Furthermore, simulation results of the proposed EMD filter show superior performance over other EMD de-noising techniques with real biomedical recordings corrupted by colored noise.

Multi-scale eigenvalues Empirical Mode Decomposition for geomagnetic signal filtering

Geomagnetic signals are susceptible to random magnetic signals and short-term, high-amplitude magnetic signals. These interferences can bring nonlinear error and degrade the navigation accuracy. Traditional Empirical Mode Decomposition (EMD) can reduce the nonlinear error of geomagnetic signal. However, with the mode mixing and the poor stability of finding dividing point by using energy criterion, traditional EMD filter is limited. In this paper, multi-scale eigenvalues EMD (ME-EMD) is proposed. To solve the problem of mode mixing, multi-scale eigenvalues are analyzed to extract the interference signal. To find the precise dividing point, autocorrelation ratio is defined. ME-EMD estimates the SNR of intrinsic mode function (IMF) and finds the dividing point. Experiments demonstrate that ME-EMD can restrain the mode mixing and find the optimal dividing point, and the filter effect of ME-EMD is better than EMD with morphology, and Modified Ensemble EMD, etc., when the geomagnetic signal is interfered by transient signal. ME-EMD reduced the Root Mean Square Error from 23.3041 µT to 1.2689 µT.

A Novel Thresholding Methodology Using WSI EMD and Adaptive Homomorphic Filter

In this brief, we propose a novel thresholding methodology using a combination an adaptive homomorphic filter (AHF) and a weighted sinc interpolated empirical mode decomposition (EMD) model for medical and nonuniformly illuminated image datasets. In this methodology, we use an AHF to correct the illumination of an image. Later, we obtain a smooth data component from the histogram of an illumination corrected image using weighted sinc interpolated EMD model. From this smooth data component, we obtain an optimal threshold value using global minimum criteria. By using this optimal threshold value, we obtain a thresholded image from the illumination corrected image. The proposed methodology is very simple and more robust to noise and easy for hardware implementation. The performance of the proposed thresholding methodology using weighted sinc interpolated EMD model is compared with other methods in the literature and the results clearly demonstrate the superiority of the proposed methodology for thresholding.

Simultaneous de-noising and enhancement method for long-span bridge health monitoring data based on empirical mode decomposition and fractal conservation law

This paper introduces a new signal-filtering method, which is aimed at processing long-span bridge structural health monitoring (SHM) data based on empirical mode decomposition (EMD) and fractal conservation law (FCL). The long-span bridge SHM data is selected from vertical vibration data of the Runyang Bridge’s main girder. The key idea of this paper is to enhance the frequency amplitude of the target signal and at the same time to weaken the surroundings-caused signal noise by combining methods as EMD and FCL after confirming the long-span bridge’s natural frequency. EMD operation consists of decomposing the vibration data by EMD, selection of effective intrinsic mode functions (IMFs) and effective IMF recombination. FCL theory will be applied on the filtering function to process effective IMFs in the frequency domain before recombination. This theory is based on a partial differential equation modified by a nonlocal fractional anti-diffusive term of lower order. The effect of the EMDFCL filter is compared with the EMD method, FCL method and filter strategy that combines EMD and low-pass filtering, respectively. Results show that the EMDFCL filter outperforms the other signal filtering methods in natural frequency recognition of long-span bridge SHM data. In particular, the bridge’s natural frequencies distinguished by EMDFCL match well with the calculated frequencies and original fingerprint.

Application of Mathematical Morphological Filtering to Improve the Resolution of Chang’E-3 Lunar Penetrating Radar Data

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.

Combined effects of PPG preprocess and dynamic spectrum extraction on predictive performance of non-invasive detection of blood components based on dynamic spectrum

The non-invasive detection accuracy of blood components based on dynamic spectrum (DS) not only depends on the accuracy of each step individually, but also the proper cooperation between these steps. Therefore, combined effects of PPG preprocess and DS extraction on the non-invasive detection of hemoglobin (Hb) were investigated in this paper: DS extraction method itself has some but insufficient ability of noise suppression, and therefore PPG preprocess method should be chosen properly to complement DS extraction method’s deficiency in noise suppression and further improve the detection accuracy. In this study, zero phase-shift bandpass filter, wavelet filter and Empirical Mode Decomposition (EMD) filter, were applied for PPG preprocess and single-trial estimation was applied for DS extraction. Partial Least Square Regression (PLSR) was applied for model establishment between DS and Hb content. The results showed that in comparison with non-preprocess, the root mean square error of prediction set (RMSEP) is reduced from 14.1003 g/L to 10.7270 g/L, 11.1018 g/L, 11.2768 g/L respectively with zero phase-shift bandpass filter, wavelet filter, EMD filter. As a result, zero phase-shift bandpass filter cooperates with single-trial estimation better in noise suppression to improve the prediction accuracy of Hb content. By studying the combined effects, we could know how to combine various methods of each step together to obtain the best calibration model. This study also has referential significance to quantitative analysis with NIR spectroscopy.

Characteristics of BeiDou Navigation Satellite System Multipath and Its Mitigation Method Based on Kalman Filter and Rauch-Tung-Striebel Smoother

Global Navigation Satellite System (GNSS) carrier phase measurement for short baseline meets the requirements of deformation monitoring of large structures. However, the carrier phase multipath effect is the main error source with double difference (DD) processing. There are lots of methods to deal with the multipath errors of Global Position System (GPS) carrier phase data. The BeiDou navigation satellite System (BDS) multipath mitigation is still a research hotspot because the unique constellation design of BDS makes it different to mitigate multipath effects compared to GPS. Multipath error periodically repeats for its strong correlation to geometry of satellites, reflective surface and antenna which is also repetitive. We analyzed the characteristics of orbital periods of BDS satellites which are consistent with multipath repeat periods of corresponding satellites. The results show that the orbital periods and multipath periods for BDS geostationary earth orbit (GEO) and inclined geosynchronous orbit (IGSO) satellites are about one day but the periods of MEO satellites are about seven days. The Kalman filter (KF) and Rauch-Tung-Striebel Smoother (RTSS) was introduced to extract the multipath models from single difference (SD) residuals with traditional sidereal filter (SF). Wavelet filter and Empirical mode decomposition (EMD) were also used to mitigate multipath effects. The experimental results show that the three filters methods all have obvious effect on improvement of baseline accuracy and the performance of KT-RTSS method is slightly better than that of wavelet filter and EMD filter. The baseline vector accuracy on east, north and up (E, N, U) components with KF-RTSS method were improved by 62.8%, 63.6%, 62.5% on day of year 280 and 57.3%, 53.4%, 55.9% on day of year 281, respectively.

Fuzzy Empirical Mode Decomposition for Smoothing Wind Power with Battery Energy Storage System

The intermittency and fluctuations of wind power cause problems in integration of wind energy systems into the grid. To smooth the fluctuations of wind power, a control scheme based on fuzzy empirical mode decomposition (EMD) is proposed with the use of battery energy storage system (BESS). With this idea, the wind power signal is decomposed via EMD and filtered into two parts: the low-frequency part is taken as the target smooth wind power for grid connection, and the high-frequency part is stored by BESS before connecting to the grid to compensate for the fluctuations in the original wind power signal. A fuzzy EMD filter is developed by considering constraints on both the power fluctuation rate and the state of charge (SOC) of the BESS. The performance of the proposed strategy has been examined through case study simulations, which demonstrates improvement in mitigating wind power fluctuations and also in reducing over charging/discharging operations of the integrated BESS.

EMG Signal Filtering Based on Independent Component Analysis and Empirical Mode Decomposition for Estimation of Motor Activation Patterns

A method based on independent component analysis (ICA) and empirical mode decomposition (EMD) for processing electromyographic (EMG) signals is proposed. This method is used for determining the motor activation pattern of the lower extremities during walking. The method is evaluated by recording EMG signals from 11 healthy women. The EMG signals used for the analysis are recorded from 16 muscles of both lower limbs during walking. The method consists of preprocessing the EMG signals using principal component analysis, analyzing them using ICA, and finally filtering them using EMD. This approach allows the reconstruction of the original source signals by filtering the components that do not have a muscular origin. These data are then segmented and processed using the Hilbert transform in order to obtain a representation of a gait cycle from all records. The activation patterns obtained with this method are compared to the ones obtained using a conventional method, based on low-pass filtering, and a method based only on EMD filtering. According to the results, the proposed method obtains a better sequence of motor activation during walking. The proposed method is validated by a comparison of the results with kinematic behavior (expressed as the angular movement of the hip, knee, and ankle of each participant) and statistical significance analysis.

Filtering SAR interferometric phase noise using a split-window model

ABSTRACTFor interferometric synthetic aperture radar (InSAR) processing, the features of interferometric phase obtained in different coherence regions usually differ from each other. This is called region effect and exists in InSAR coherence map. When coherence value is used as a parameter to filter the phase noise, the result will be highly affected by this region effect. In this paper, we propose a new method of filtering InSAR phase noise using a split-window model. The idea of this method is to incorporate several filters into the model. Different filters will be used when dealing with phase noise locates in different coherence regions. The over-filtering or under-filtering caused by the coherence region effect can be eliminated in this method. As an example to demonstrate the superiority of this method, we incorporated an improved Goldstein filter and empirical mode decomposition filter into the current model. They were included to control phase noise level in the low- and high-coherence regions, respectively. The quantitative results obtained using a COnstellation of small Satellites for the Mediterranean basin Observation (COSMO-SkyMed) image pair over Kilauea volcano in Hawaiian demonstrate the advantages of the newly developed split-window model in filtering different types of noise.

Three-dimensional underwater target localization with circular vector sensor array in strong reverberation environment

As reverberation can be regarded as an output of time-varying stochastic filtering of the emitted signal, it always leads to high false alarm in shallow water. In this case, some methods have been used to suppress reverberation such as empirical mode decomposition filter, match filter, etc. However, a strong correlation between transmitted signal and reverberation restricts the performance of these methods. In this paper,a novel method based on a circular vector sensor array is proposed to detect and locate multiple targets, respectively, in a strong reverberation environment by using the difference of the Doppler shift distribution between reverberation and target echo. First, spherical harmonic decomposition and beamforming are used to process the wideband signal and generate multiple beams which contain spatial information of interested region. Then, fractional Fourier transform is adopted to get Doppler shift distribution and localization information of reverberation and target echo. Multiple targets can be detected and located separately by estimating variance fluctuation associated with the reflections in different beams. Simulation result shows that this method performs efficiently and reliably in server reverberation environment without any prior information.

SAR interferometric phase filtering technique based on bivariate empirical mode decomposition

The empirical mode decomposition (EMD) has been widely applied in filtering synthetic aperture radar interferograms. A noisy interferogram can be adaptively decomposed into different frequency modes by EMD. Then, the noise can be eliminated based on the partial reconstruction of relevant modes. However, most fine detail and noise of an interferogram often locate in the same mode, which will lead to an inaccurate estimation of noise level in a local region. In this paper, we proposed an improved filtering method based on bivariate EMD. The idea of our method is to decompose both the phase image and pseudo-coherence map of an interferogram using EMD. The filter level of an interferogram is then controlled by the parameters calculated from the bivariate EMD components. The quantitative results from both simulated and real data show that the bivariate EMD filtering method outperforms the original univariate EMD-based methods. It could achieve a balance between suppressing noise and preserving fine detail of an interferogram.

Noisy Reverberation Suppression Using AdaBoost Based EMD in Underwater Scenario

Reverberation suppression is a crucial problem in sonar communications. If the acoustic signal is radiated in the water as medium then the degradation is caused due to the reflection coming from surface, bottom, and volume of water. This paper presents a novel signal processing scheme that offers an improved solution in reducing the effect of interference caused due to reverberation. It is based on the combination of empirical mode decomposition (EMD) and adaptive boosting (AdaBoost) techniques. AdaBoost based EMD filtering technique is used for reverberation corrupted chirp signal to decrease the noisy components present in the received signal. An improvement in the probability of detection is achieved using the proposed algorithm. The simulation results are obtained for various reverberation times at various SNR levels.

Performance improved method for subtracted blood volume spectrometry using empirical mode decomposition

Subtracted blood volume spectrometry (SBVS) can eliminate the background information in near infrared spectroscopy (NIRS) noninvasive biochemical sensing. However, the spectrum obtained by this method is accompanied by serious noises which are to the disadvantage of the calibration models. Empirical mode decomposition (EMD) was applied to restrict the noises in order to improve the performance of subtracted blood volume spectrometry. Certain criteria were used to evaluate the performance of the method, such as the average correlation coefficient, and the average and standard deviation of the Euclidean distance. EMD was applied to three subtracted spectra with different ΔL, and the criteria were calculated accordingly. All of the criteria were improvement. Especially for the subtracted spectra with ΔL=0.5mm, the correlation coefficient increased from 0.9970 to 0.9999, the average Euclidean distance decreased from 0.0265 to 0.0118, and the standard deviation of the Euclidean distance decreased from 0.0148 to 0.0033 after EMD filtering. The PLS models of the processed spectra were promoted as well. These preliminary results suggest that EMD is a promising means of improving the performance of subtracted blood volume spectrometry.

THEORY OF DIGITAL FILTER BANKS REALIZED VIA MULTIVARIATE EMPIRICAL MODE DECOMPOSITION

Undecimated and decimated multivariate empirical mode decomposition filter banks (MEMDFBs) are introduced in order to incorporate MEMD equipped with downsampling into any arbitrary tree structure and provide flexibility in the choice of frequency bands. Undecimated MEMDFBs show the same results as those of original MEMD for an octave tree structure. Since the exact cut-off frequencies of MEMD are not known (i.e. due to data-driven decomposition), employing just simple downsampling in MEMD might cause aliasing. However, decimated MEMDFBs in this paper achieve perfect reconstruction with aliasing cancelled for any arbitrary tree. Applications of decimated/undecimated MEMDFBs for speech/audio and image signals are also included. Since decimated MEMDFBs can be applied into any arbitrary tree structure, this extends into MEMD packets. Arbitrary tree structures in decimated MEMDFBs also lead to more diverse choices in frequency bands for various multivariate applications requiring decimations.

Empirical mode decomposition-based monopulse processor for enhanced radar tracking in the presence of high-power interference

Monopulse radars are used to track targets that appear in the look direction beam width. The distortion of the tracking information produced when high power interference signals (jamming) are introduced through the radar antenna can cause severe erroneous outcomes from the monopulse tracking processor. This leads to errors in the target tracking angles that may cause target mistracking. New monopulse radar structures that utilizes an empirical mode decomposition (EMD) based denoising processor are presented in this paper. EMD filtering is carried out for the complex radar chirp signal with subsequent detrending, thresholding, and denoising processes. These processes are used to decrease the noise level in the radar processed data to improve the signal to noise ratio. The performance enhancement using the monopulse radar tracking system with EMD based filtering is included using the standard deviation angle estimation error (STDAE) for different jamming scenarios and different target SNRs. STDAE shows that the new EMD based system works well in the case of main beam interference and side lobe interference for the conventional processor and, in the case of the spatial adaptive processor, is effective in the case of main lobe interference.