In this paper, a blind signal separation (BSS) methodology for simultaneously received multisystem frequency-overlapped signals in a single-channel (SC) electromagnetic surveillance system is proposed using fast independent component analysis (FastICA) in a dynamical embedding (DE) framework. Firstly, an appropriate DE matrix is constructed out of a series of delay vectors from the SC recording. The lag-time and the dimensional of embedding matrix setting principal are introduced in details. Next, multiple independent components (ICs) are calculated by decomposing the embedding matrix through FastICA algorithm, and ICs can be regarded as a convenient expansion basis of the original signals. Then, these ICs are projected back into the measurement space. After that, these projected ICs are classified and used for recovering the sources of interest based on their independent nature and their power density spectrum. Numerical simulation results obtained in evaluating the proposed methodology's performance confirmed the effectiveness of the proposed algorithm.
To simulate the condition of unstable rock formation in the stable rock formation of the roadway roof, the complete and broken rock formations on the roadway roof were simulated by superimposing and combining complete specimens and broken specimens, hence providing an appropriate way to obtain the drilling vibration signals to find regular patterns which can be used to judge the state of the rock layers. The Fourier transform analysis method and the wavelet transform analysis method are, respectively, used for the vibration signals to extract the eigenvalues of the vibration signals that are quite different in the complete layer and broken layers. Effective values, kurtosis factor, pulse factors, and gravity amplitude were used to construct the eigenvectors, which were most suitable as the basis for judging the integrity of rock formation after analysis, providing an effective way to deal with drilling signal. The wavelet threshold denoising could adaptively reduce the noise of the signals, and the unbiased likelihood estimation threshold rule had the best denoising effect. Well, through the extraction and comparison of the wavelet feature-scale entropy values, it is concluded that the wavelet analysis can be used as a reference rather than a decisive factor to determine the complete and broken layers. And more research is needed to determine the selection method of wavelet.
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