Non-commutative time-frequency tomography
V. I. Man'ko, R. Vilela Mendes
Abstract
The characterization of non-stationary signals requires joint time and frequency information. However, time (t) and frequency (omega) being non-commuting variables there cannot be a joint probability density in the (t,omega) plane and the time-frequency distributions, that have been proposed, have difficult interpretation problems arising from negative or complex values and spurious components. As an alternative we propose to obtain time-frequency information by looking at the marginal distributions along rotated directions in the (t,omega) plane. The rigorous probability interpretation of the marginal distributions avoids all interpretation ambiguities. Applications to signal analysis and signal detection are discussed as well as an extension of the method to other pairs of non-commuting variables.
Create a lesson
Related papers
Reduced latent leakage does not reliably predict lower likelihood bias in collider inference
Tong Pan
The Greedy Bump Bias: Local Profiling Geometry and the Look-Elsewhere Effect
Tommaso Dorigo
Multi-fidelity Monte Carlo estimation of floor response spectra under combined seismic and structural parameter uncertainties
Nils Baillie, Baptiste Kerleguer, Cyril Feau et al.
Parameter inference from a non-stationary unknown process using statistical feature-based slow feature analysis
Kieran S. Owens, Masako Tamaki, Ben D. Fulcher
A Probability Model for Pentagonal Prism Dice Rolls
Paul R. Hurst, J. Naleo Hyde
Geometry-native machine learning reconstruction of DSMC moment fields with support monitoring
Ehsan Roohi