Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Time-frequency analysis: theory and applications
Time-frequency analysis: theory and applications
Discrete-time signal processing (2nd ed.)
Discrete-time signal processing (2nd ed.)
Signal Processing - From signal processing theory to implementation
Comments on “The Cramer-Rao lower bounds for signals withconstant amplitude and polynomial phase”
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
The homomorphic analytic signal
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Auditory motivated level-crossing approach to instantaneous frequency estimation
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
Signal-to-noise ratio estimation using higher-order moments
Signal Processing
An adaptive resolution computationally efficient short-time Fourier transform
Research Letters in Signal Processing
Adaptive rate sampling and filtering based on level crossing sampling
EURASIP Journal on Advances in Signal Processing
An improved phasor based algorithm for accurate frequency measurement
Mathematical and Computer Modelling: An International Journal
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We address the problem of estimating instantaneous frequency (IF) of a real-valued constant amplitude time-varying sinusoid. Estimation of polynomial IF is formulated using the zero-crossings of the signal. We propose an algorithm to estimate nonpolynomial IF by local approximation using a low-order polynomial, over a short segment of the signal. This involves the choice of window length to minimize the mean square error (MSE). The optimal window length found by directly minimizing the MSE is a function of the higher-order derivatives of the IF which are not available a priori. However, an optimum solution is formulated using an adaptive window technique based on the concept of intersection of confidence intervals. The adaptive algorithm enables minimum MSE-IF (MMSE-IF) estimation without requiring a priori information about the IF. Simulation results show that the adaptive window zero-crossing-based IF estimation method is superior to fixed window methods and is also better than adaptive spectrogram and adaptive Wigner-Ville distribution (WVD)-based IF estimators for different signal-to-noise ratio (SNR).