Estimation of FM signal parameters in impulse noise environments
Signal Processing
Multicomponent chirp signals analysis using product cubic phase function
Digital Signal Processing
Fast algorithms for polynomial time frequency transform
Signal Processing
Robust OFDM system with ml-HIM encoding
EURASIP Journal on Wireless Communications and Networking
EURASIP Journal on Applied Signal Processing
Time-frequency analysis using warped-based high-order phase modeling
EURASIP Journal on Applied Signal Processing
Adaptive local polynomial fourier transform in ISAR
EURASIP Journal on Applied Signal Processing
A new class of multilinear functions for polynomial phase signal analysis
IEEE Transactions on Signal Processing
Long-range channel prediction based on nonstationary parametric modeling
IEEE Transactions on Signal Processing
Adaptive algorithm for chirp-rate estimation
EURASIP Journal on Advances in Signal Processing
Parameter estimation of phase-modulated signals using Bayesian unwrapping
IEEE Transactions on Signal Processing
Autofocusing of SAR images based on parameters estimated from the PHAF
Signal Processing
New approach for ISAR imaging of ship target with 3D rotation
Multidimensional Systems and Signal Processing
Localization in underwater dispersive channels using the time-frequency-phase continuity of signals
IEEE Transactions on Signal Processing
Estimating multiple frequency-hopping signal parameters via sparse linear regression
IEEE Transactions on Signal Processing
Performance of instantaneous frequency rate estimation using high-order phase function
IEEE Transactions on Signal Processing
A signal-dependent quadratic time frequency distribution for neural source estimation
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
STFT-based estimator of polynomial phase signals
Signal Processing
Are genetic algorithms useful for the parameter estimation of FM signals?
Digital Signal Processing
| Hi-index | 35.70 |
Parameter estimation and performance analysis issues are studied for multicomponent polynomial-phase signals (PPSs) embedded in white Gaussian noise. Identifiability issues arising with existing approaches are described first when dealing with multicomponent PPS having the same highest order phase coefficients. This situation is encountered in applications such as synthetic aperture radar imaging or propagation of polynomial phase signals through channels affected by multipath and is thus worthy of a careful analysis. A new approach is proposed based on a transformation called product high-order ambiguity function (PHAF). The use of the PHAF offers a number of advantages with respect to the high-order ambiguity function (HAF). More specifically, it removes the identifiability problem and improves noise rejection capabilities. Performance analysis is carried out using the perturbation method and verified by simulation results