Augmented EMD for complex‐valued univariate signals

Oh, Beom Seok; Zhuang, Huiping; Toh, Kar-Ann; Lin, Zhiping

doi:10.1049/iet-spr.2018.5428

Cited by 4 publications

(3 citation statements)

References 28 publications

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“…Another attempt made to resolve the low resolution issue was to decompose radar echo signals using the EMD method which is a non-Fourier-based time-frequency analysis technique (Bai et al, 2008;Du et al, 2013;Wang et al, 2016;Oh et al, 2018;Ma et al, 2018;Oh et al, 2019). By extracting discriminative features from a set of intrinsic mode functions which are obtained by the EMD method (see Section 3.1 for details), a better UAV classification accuracy than that of the Fourier-based approaches has been achieved (Oh et al, 2018).…”

Section: A Brief Literature Review On Time-frequency Analysis Methods Used For the M-ds Analysismentioning

confidence: 99%

A UAV classification system based on FMCW radar micro-Doppler signature analysis

Guo²,

Lin

2019

Expert Systems with Applications

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Section: A Brief Literature Review On Time-frequency Analysis Methods Used For the M-ds Analysismentioning

confidence: 99%

A UAV classification system based on FMCW radar micro-Doppler signature analysis

Guo²,

Lin

2019

Expert Systems with Applications

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“…e EMD algorithm can discretize the random nonstationary time series into a high-frequency and a low-frequency signals sequence, representing the overall change trend and random disturbance, respectively. It can completely retain the information of the original time series [23].…”

Section: Establishment Of the Emd-gm-arma Modelmentioning

confidence: 99%

EMD-GM-ARMA Model for Mining Safety Production Situation Prediction

et al. 2020

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In order to improve the prediction accuracy of mining safety production situation and remove the difficulty of model selection for nonstationary time series, a grey (GM) autoregressive moving average (ARMA) model based on the empirical mode decomposition (EMD) is proposed. First of all, according to the nonstationary characteristics of the mining safety accident time series, nonstationary original time series were decomposed into high- and low-frequency signals using the EMD algorithm, which represents the overall trend and random disturbances, respectively. Subsequently, the GM model was used to predict high-frequency signal sequence, while the ARMA model was used to predict low-frequency signal sequence. Finally, aiming to predict the mining safety production situation, the EMD-GM-ARMA model was constructed via superimposing the prediction results of each subsequence, thereby compared to the ARIMA model, wavelet neural network model, and PSO-SVM model. The results demonstrated that the EMD-GM-ARMA model and the PSO-SVM model hold the highest prediction accuracy in the short-term prediction, and the wavelet neural network has the lowest prediction accuracy. The PSO-SVM model’s prediction accuracy decreases in medium- and long-term predictions while the EMD-GM-ARMA model still can maintain high prediction accuracy. Moreover, the relative error fluctuations of the EMD-GM-ARMA model are relatively stable in both short-term and medium-term predictions. This shows that the EMD-GM-ARMA model can provide high-precision predictions with high stability, proving the model to be feasible and effective in predicting the mining safety production situation.

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“…The algorithms for denoising dynamic weighing data mainly include empirical mode decomposition 3 , wavelet transform algorithm 4 , and digital filtering method 5 . Empirical mode decomposition decomposes the output signal from the dynamic weighing sensor into several intrinsic mode functions (IMFs) that contain the dynamic characteristics of the signal and a residual component that contains the steady-state values.…”

Section: Introductionmentioning

confidence: 99%

Dynamic weighing algorithm for unmanned aerial vehicles based on filtering fusion

Deng,

Zhu,

Bai

2024

Fifteenth International Conference on Signal Processing Systems (ICSPS 2023)

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In agriculture, obtaining the remaining fertilizer weight is crucial for achieving accurate fertilizer application when using drones for spreading fertilizer. To address the problem of inaccurate weighing of remaining fertilizer caused by environmental and system factors during the fertilizer application process, a fusion filtering algorithm suitable for dynamic weighing of fertilizer is proposed. First, a second-order model is established between the wheel speed and the fertilizer application rate. The three coefficients in the second-order mapping relationship formula between the fertilizer weight and the wheel speed are used as the four-dimensional state variables of the Kalman filter, with the wheel speed on the drone set as the control variable. Then the weight data measured by the weighing sensor is sent to the Kalman filter for primary filtering. At the same time, the sliding average filtering algorithm is used to smooth out the oscillation phenomenon in the filtering results. Simulation experiments and real data processing results show that the drone dynamic weighing fusion algorithm, based on the four-dimensional Kalman filter and sliding average filtering has good noise reduction and smoothing effects on fertilizer weight data.

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Augmented EMD for complex‐valued univariate signals

Cited by 4 publications

References 28 publications

A UAV classification system based on FMCW radar micro-Doppler signature analysis

A UAV classification system based on FMCW radar micro-Doppler signature analysis

EMD-GM-ARMA Model for Mining Safety Production Situation Prediction

Dynamic weighing algorithm for unmanned aerial vehicles based on filtering fusion

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