KEYWORDS: General packet radio service, Interference (communication), Signal processing, Signal to noise ratio, Principal component analysis, Time-frequency analysis, Signal analysis, Signal analyzers, Signal detection, Error analysis
In this paper, we apply a time and frequency analysis method based on the complete ensemble empirical mode decomposition (CEEMD) in GPR signal processing. It decomposes the GPR signal into a sum of oscillatory components, with guaranteed positive and smoothly varying instantaneous frequencies. The key idea of this method relies on averaging the modes obtained by EMD applied to several realizations of Gaussian white noise added to the original signal. It can solve the mode mixing problem in empirical mode decomposition (EMD) method and improve the resolution of ensemble empirical mode decomposition (EEMD) when the signal has low signal noise ratio (SNR). First, we analyze the difference between the basic theory of EMD, EEMD and CEEMD. Then, we compare the time and frequency analysis results of different methods. The synthetic and real GPR data demonstrate that CEEMD promises higher spectral-spatial resolution than the other two EMDs method. Its decomposition is complete, with a numerically negligible error.
The human’s Micro-Doppler signatures resulting from breathing, arm, foot and other periodic motion can provide
valuable information about the structure of the moving parts and may be used for identification and classification
purposes. In this paper, we carry out simulate with FDTD method and through wall experiment with UWB radar for
human being’s periodic motion detection. In addition, Advancements signal processing methods are presented to classify
and to extract the human’s periodic motion characteristic information, such as Micro-Doppler shift and motion
frequency. Firstly, we apply the Principal Component Analysis (PCA) with singular value decomposition (SVD) to denoise
and extract the human motion signal. Then, we present the results base on the Hilbert-Huang transform (HHT) and
the S transform to classify and to identify the human’s micro-Doppler shift characteristics. The results demonstrate that
the combination of UWB radar and various processing methods has potential to detect human’s Doppler signatures
effectively.
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