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Empirical Mode Decomposition (EMD) is an effective, non-linear and non-stationary data analysis method, which can decompose the original signal into several intrinsic mode functions(IMFs). However the frequency resolution of EMD has not been thoroughly investigated so far. In this paper a signal which contains two different frequency components was do(More)
First, the false peak of Burg Spectrum estimation is introduced. In this paper, a new method of eliminating false peak based on Empirical Mode Decomposition is proposed. Secondly, this new method is used in the Line spectrum analysis of ship radiated noise simulation signal. Compared with traditional Linear Spectrum analysis, it is a better method for(More)
It is greatly significant to detect harmonic accurately and effectively for improving the quality of electric energy in the power system. Actually, much noise exists in signal besides harmonics, inter-harmonics, so the key is how to detect harmonic signals from the complex power system. According to the analysis of harmonic and noise, a new method is(More)
In order to solve the signal during transmission on the external mixture of noise, this paper presents the wavelet de-noising based on empirical mode decomposition (EMD), with Wu and others view that the previous order intrinsic mode function (IMF) components need for the wavelet de-noising, and with the wavelet threshold de-noising method and directly(More)
The empirical mode decomposition (EMD) was recently proposed as a new time-frequency analysis tool for non-stationary and non-linear signals. Firstly, In this paper a new signal analysis method of adaptive filter-EMD-LMS is introduced. Secondly, spectrum analysis of simulated signals are used based on EMD-LMS. Compared with traditional spectrum analysis(More)
This paper introduces the advantages of a new type of radar signal. This signal adopts the linear frequency modulation in the pulse modulation mode and phase coded modulation method between pulses. In this paper, we use the ambiguity function theory and LPI theory to analyses the composite radar signal. Analysis result shows the superiority of the signal.
Hilbert-Huang Transform(HHT) is widely used in extracting the instantaneous characteristics of signals. If using Hilbert Transform to deal with signals, the signals must Satisfy certain conditions, or else there will be errors in extracting the instantaneous characteristics of general signals. To solve this problem, a new method was proposed which combines(More)
The noise image is decomposed into the unknown true image u and the noise v by a new image de-noising method based on image decomposition. The common decomposition models are all dense and can only be transformed into high-order partial differential equations to solve, which are heavy computations. DT model and the Jiang model are sparse image decomposition(More)
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