Classification of nonstationary narrowband signals using segmented chirp features and hidden Gauss-Markov models

A method is provided for classifying finite-duration signals with narrow instantaneous bandwidth and dynamic instantaneous frequency (IF). In this method, events are partitioned into nonoverlapping segments, and each segment is modeled as a linear chirp, forming a piecewise-linear IF model. The star...

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Veröffentlicht in:	IEEE transactions on signal processing 2005-01, Vol.53 (1), p.147-157
Hauptverfasser:	Ainsleigh, P.L., Greineder, S.G., Kehtarnavaz, N.
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Applied sciences Bandwidth Chirp Classification Covariance scaling Detection, estimation, filtering, equalization, prediction EM algorithm Exact sciences and technology fractional Fourier transform Frequency estimation Gaussian approximation Gaussian processes hidden Markov model Information, signal and communications theory Intermediate frequency linear chirp linear Gaussian model Mathematical models Narrowband Newton algorithm Noise levels Parameter estimation piecewise linear FM Piecewise linear techniques Probability density function Probability density functions Segments Signal and communications theory signal classification Signal to noise ratio Signal, noise SNR weighting Studies Telecommunications and information theory
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