Comments on “The Cramer-Rao lower bounds for signals withconstant amplitude and polynomial phase”
IEEE Transactions on Signal Processing
An overview of aliasing errors in discrete-time formulations oftime-frequency representations
IEEE Transactions on Signal Processing
The Cramer-Rao lower bound for signals with constant amplitude andpolynomial phase
IEEE Transactions on Signal Processing
Alias-free generalized discrete-time time-frequency distributions
IEEE Transactions on Signal Processing
Radon transformation of time-frequency distributions for analysisof multicomponent signals
IEEE Transactions on Signal Processing
Linear frequency-modulated signal detection using Radon-ambiguitytransform
IEEE Transactions on Signal Processing
Product high-order ambiguity function for multicomponentpolynomial-phase signal modeling
IEEE Transactions on Signal Processing
Analysis of multicomponent LFM signals by a combined Wigner-Houghtransform
IEEE Transactions on Signal Processing
IEEE Transactions on Image Processing
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Signal detection and parameter estimation for mono- and multicomponent linear frequency modulation (LFM) signals are studied by using the keystone transform of the Wigner-Ville distribution (WVD). The keystone-Wigner transform (KWT) introduces a weight factor containing a range of chirp rate into the time-lag instantaneous autocorrelation function and uses a one-dimensional (1-D) interpolation of the phase which we call keystone formatting. The proposed processing eliminates the effects of linear frequency migration (i.e., the frequency linearly varies along the time axis) to all the signal components even if their chirp rates are unknown. The Fourier transform (FFT) over the time variable to results of the KWT make the power of multicomponent LFM signal concentrated as the locations corresponding to their parameters. Furthermore, the KWT can be efficiently implemented using only complex multiplications and FFT based on the scaling principle instead of interpolating. The computational complexity of KWT is O(4N^2log"2N). Performance analysis is presented by using the perturbation method and verified by simulation results. Finally, the effectiveness of the KWT is validated by a real application example.