Adaptive time-frequency analysis based on autoregressive modeling

Authors:
Antonio H. Costa;Stephan Hengstler
Affiliations:
Department of Electrical and Computer Engineering, University of Massachusetts, Dartmouth, MA 02747, USA;Alverix, Inc., 2590 North First Street, San Jose, CA 95131, USA
Venue:
Signal Processing
Year:
2011

Citing 8
Cited 3

An AR spectral analysis of non-stationary signals

Signal Processing
Discrete-time signal processing (2nd ed.)

Discrete-time signal processing (2nd ed.)
Adaptive Filters: Theory and Applications

Adaptive Filters: Theory and Applications
Advanced Topics in Digital Signal Processing

Advanced Topics in Digital Signal Processing
Time-frequency representation for time-varying signals using a Kalman filter

ASILOMAR '95 Proceedings of the 29th Asilomar Conference on Signals, Systems and Computers (2-Volume Set)
Adaptive diffusion as a versatile tool for time-frequency and time-scale representations processing: a review

IEEE Transactions on Signal Processing
An adaptive optimal-kernel time-frequency representation

IEEE Transactions on Signal Processing
Structural stability of least squares prediction methods

IEEE Transactions on Signal Processing

Boundary extension for Hilbert-Huang transform inspired by gray prediction model

Signal Processing
Deterministic regression methods for unbiased estimation of time-varying autoregressive parameters from noisy observations

Signal Processing
IF estimation of FSK signals using adaptive smoothed windowed cross Wigner-Ville distribution

Signal Processing

Quantified Score

Hi-index	0.08

Visualization

Abstract

A new adaptive method for discrete time-frequency analysis based on autoregressive (AR) modeling is introduced. The performance of AR modeling often depends upon a good selection of the model order. The predictive least squares (PLS) principle of Rissanen was found to be a good criterion for model order estimation in stationary processes. This paper presents a modified formulation of the PLS criterion suitable for non-stationary processes. Efficient lattice filters based on the covariance assumption are used to estimate the model parameters of all model orders less than some maximum order M. It is shown that the resulting complexity is no larger than M. The modified PLS criterion allows the model order to adapt to non-stationary processes and, in turn, compute adaptive AR based time-frequency representations (TFRs). Examples of time-frequency analyses for synthetic and bio-acoustical signals are provided as well as comparisons to classical time-frequency representations.