By Frank Fahy, John Walker
Complex purposes in Noise and Vibration provides remedies of acoustic and vibration phenomena including summaries of the most recent wisdom, research and strategies at the moment on hand for facing useful difficulties.
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Extra resources for Advanced Applications in Acoustics, Noise and Vibration
R. K. 84) This is the estimate of the power of the signal in the ‘close’ vicinity of f Hz and constitutes the Capon spectral estimate. 7 Eigen-based high-resolution methods The problem of detecting and estimating sinusoidal signals in noise has received enormous attention, and the so-called eigen-based methods have evolved around this problem (see, for example, Vaseghi 1996). The eigenvalues and eigenvectors are computed from the autocorrelation matrix of the measured signal (sinusoid plus noise) and allow the decomposition of the measured signal into components lying in the (true) signal subspace and the noise subspace (orthogonal to the signal subspace).
18b a shorter window is used which allows one to observe the underlying structure of this signal. The two curves describing the arrival times of frequency components in each of the wave packets can be seen. 17 Beam response measurement. R. K. 18 Time–frequency distributions for beam response data. measurements from these curves, allied to knowledge of the spacing between the site of excitation and measurement, allow one to estimate the dispersion laws for this beam. 18c depicts the WVD for this data.
1976) An empirical investigation of the properties of the autoregressive spectral estimator. IEEE Transactions on Information Theory, IT-22, 313–323. R. K. M. (1988) Modern Spectral Estimation: Theory and Application (Englewood Cliffs: Prentice-Hall). G.
Advanced Applications in Acoustics, Noise and Vibration by Frank Fahy, John Walker