signals with time-varying spectral content arise in a number of situations, such as in shallow water sound propagation, biomedical signals, machine and structural vibrations, and seismic signals, among others. The Wig...
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ISBN:
(纸本)0819463922
signals with time-varying spectral content arise in a number of situations, such as in shallow water sound propagation, biomedical signals, machine and structural vibrations, and seismic signals, among others. The Wigner distribution and its generalization have become standard methods for analyzing such time-varying signals. We derive approximations of the Wigner distribution that can be applied to gain insights into the effects of filtering, amplitude modulation, frequency modulation, and dispersive propagation on the time-varying spectral content of signals.
We argue that the standard definition of signal to noise ratio may be misleading when the signal or noise are nonstationary. We introduce a new measure that we call local signal to noise ratio (LSNR) which is well sui...
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ISBN:
(纸本)0819463922
We argue that the standard definition of signal to noise ratio may be misleading when the signal or noise are nonstationary. We introduce a new measure that we call local signal to noise ratio (LSNR) which is well suited to take into account nonstationary situations. The advantage of our measure is that it is a local property unlike the standard SNR which is a single number representing the total duration of the signal. We simulated a number of cases to show that our measure is more indicative of the noise and signal level for nonstationary situations.
In array processing applications, it is desirable to extract the sources that generate the observed signals. There are various source separation and component extraction algorithms in literature including principal co...
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ISBN:
(纸本)0819463922
In array processing applications, it is desirable to extract the sources that generate the observed signals. There are various source separation and component extraction algorithms in literature including principal component analysis (PCA) and independent component analysis (ICA). However, most of these methods are not designed to deal with time-varying signals and thus are formulated in the time domain. In this paper, we introduce a new time-frequency based decomposition method using an information measure as the decomposition criteria. It is shown that under the assumption of disjoint source signals on the time-frequency plane, this method can extract the sources up to a scalar factor. Based on the QR decomposition of the mixing matrix, the source extraction algorithm is reduced to finding the optimal N-dimensional rotation of the observed time-frequency distributions. The proposed algorithm is implemented using the steepest descent approach to find the optimal rotation angle. The performance of the method is illustrated for example signals and compared to some well-known decomposition techniques.
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