Adaptive S-method for SAR/ISAR imaging
EURASIP Journal on Advances in Signal Processing
Conditional spectral moments in matching pursuit based on the chirplet elementary function
Digital Signal Processing
An efficient hardware design of a system for highly nonstationary signals filtering
ACACOS'08 Proceedings of the 7th WSEAS International Conference on Applied Computer and Applied Computational Science
Time--frequency feature representation using energy concentration: An overview of recent advances
Digital Signal Processing
IEEE Transactions on Signal Processing
Autofocusing of SAR images based on parameters estimated from the PHAF
Signal Processing
Journal of Signal Processing Systems
Adaptive windowed cross Wigner-Ville distribution as an optimum phase estimator for PSK signals
Digital Signal Processing
Hi-index | 35.69 |
General performance analysis of the shift covariant class of quadratic time-frequency distributions (TFDs) as instantaneous frequency (IF) estimators, for an arbitrary frequency-modulated (FM) signal, is presented. Expressions for the estimation bias and variance are derived. This class of distributions behaves as an unbiased estimator in the case of monocomponent signals with a linear IF. However, when the IF is not a linear function of time, then the estimate is biased. Cases of white stationary and white nonstationary additive noises are considered. The well-known results for the Wigner distribution (WD) and linear FM signal, and the spectrogram of signals whose IF may be considered as a constant within the lag window, are presented as special cases. In addition, we have derived the variance expression for the spectrogram of a linear FM signal that is quite simple but highly signal dependent. This signal is considered in the cases of other commonly used distributions, such as the Born-Jordan and the Choi-Williams distributions. It has been shown that the reduced interference distributions outperform the WD but only in the case when the IF is constant or its variations are small. Analysis is extended to the IF estimation of signal components in the case of multicomponent signals. All theoretical results are statistically confirmed.