Adaptive windowed Fourier transform
Signal Processing
Signal Processing - From signal processing theory to implementation
Signal-to-noise ratio estimation using higher-order moments
Signal Processing
Instantaneous frequency estimation using stochastic calculus and bootstrapping
EURASIP Journal on Applied Signal Processing
Adaptive window zero-crossing-based instantaneous frequency estimation
EURASIP Journal on Applied Signal Processing
IEEE Transactions on Signal Processing
Techniques to obtain good resolution and concentrated time-frequency distributions: a review
EURASIP Journal on Advances in Signal Processing
Adaptive algorithm for chirp-rate estimation
EURASIP Journal on Advances in Signal Processing
An improved and fast approach to parameter estimation of SFM signal using Carson's rule
WSEAS Transactions on Signal Processing
Study of ADZT properties for spectral analysis
GAVTASC'11 Proceedings of the 11th WSEAS international conference on Signal processing, computational geometry and artificial vision, and Proceedings of the 11th WSEAS international conference on Systems theory and scientific computation
A way to a new multi-spectral transform
GAVTASC'11 Proceedings of the 11th WSEAS international conference on Signal processing, computational geometry and artificial vision, and Proceedings of the 11th WSEAS international conference on Systems theory and scientific computation
STFT-based estimator of polynomial phase signals
Signal Processing
An Adaptive Window Length Strategy for Eukaryotic CDS Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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The estimation of the instantaneous frequency (IF) of a harmonic complex-valued signal with an additive noise using the Wigner distribution is considered. If the IF is a nonlinear function of time, the bias of the estimate depends on the window length. The optimal choice of the window length, based on the asymptotic formulae for the variance and bias, can be used in order to resolve the bias-variance tradeoff. However, the practical value of this solution is not significant because the optimal window length depends on the unknown smoothness of the IF. The goal of this paper is to develop an adaptive IF estimator with a time-varying and data-driven window length, which is able to provide quality close to what could be achieved if the smoothness of the IF were known in advance. The algorithm uses the asymptotic formula for the variance of the estimator only. Its value may be easily obtained in the case of white noise and relatively high sampling rate. Simulation shows good accuracy for the proposed adaptive algorithm