Parameter Estimation for Locally Linear FM Signals Using a Time-Frequency Hough Transform

Abstract

An estimator for the phase parameters of mono-and multicomponent FM signals, with both good numerical properties and statistical performance is proposed. The proposed approach is based on the Hough transform of the pseudo-Wigner-Ville timefrequency distribution (PWVD). It is shown that the numerical properties of the estimator can be improved by varying the PWVD window length. The effect of the window time extent on the statistical performance of the estimator is delineated. Experimental data is used for validation of the statistical properties.

Simultaneous measurement of in-plane and out-of-plane displacements using pseudo-Wigner-Hough transform

“…This shows that the performance of PWHT based phase estimation is far superior as compared to that proposed in [7]. The simulation study performed in [10] has shown that the PWHT based phase parameter estimation has better performance compared to product high order ambiguity function (PHAF). This indicates that the phase estimation method proposed in this study performs better compared to the method based on PHAF proposed in [6].…”

“…This led to the development of the combined use of pseudo-Wigner-Ville-Distribution (PWVD) and Hough transform, more appropriately termed as pseudo-Wigner-Hough-transform (PWHT) [10]. The PWHT of the interference field Γ l (y) can be represented as,…”

“…The optimum value of M = 0.1N L has been suggested [10] for optimal estimation of phase parameters. The number of peaks observed in the PWHT of a given multicomponent signal indicates the number of signal components present in it.…”

“…In order to achieve this, peak detection in PWHT is performed on the grid of phase parameters b and c with appropriate grid spacings of ∆ b and ∆ c , respectively. The values of ∆ b and ∆ c can be calculated as [10],…”

Analysis of Low Probability of Intercept Radar Signals Using the Reassignment Method

“…It has poorer time-frequency localization but less crossterm interference than either the WVD or CWD and its cross-terms are limited to regions where the signals overlap (Auger et al, 1996). The scalogram is the magnitude squared of the wavelet transform and can be used as a time-frequency distribution (Cohen, 2002;Galleani et al, 2006;Cirillo et al, 2008). Like the spectrogram, the scalogram has cross-terms that are limited to regions where the signals overlap (Auger et al, 1996;Hlawatsch and Boudreaux-Bartels, 1992).…”