Résumé:
Time-frequency distributions (TFDs) based on time-lag kernels with compact
support (KCS) have proved their high performance in terms of resolution and
crossterms suppression. However, as for all kernel-based quadratic TFDs,
these distributions suffer from spreading out signal terms. This is due to the
unavoidable smoothing effects of the kernel in the ambiguity domain. The
main objective of this manuscript is to improve concentration, interference
rejection and so time-frequency readability of this representation class. The
latter has the advantage of being tuned using a single parameter while external
windows are no longer needed. The KCS-TFDs, referred to as KCSDs,
are first optimized using objective performance measures used in the literature.
Important signal features are extracted as well through analysis of
time slice plots. The obtained TF diagrams are then enhanced using a specific
method that includes two-dimensionalWiener filter, automatic binarization
and morphological image processing techniques. The enhanced plots
are compared to those obtained from the original TFDs using several tests
on real-life and multicomponent frequency modulated (FM) signals including
the noise effects. Moreover, a comparative study involving a selection of
the best-performing reassignment time-frequency distributions is provided.
The obtained results show a significant improvement of concentration, timefrequency
localization of the autoterms as well as interference and noise suppression.
As viable applications, the proposed approach is used first to instantaneous
frequency (IF) estimation of several synthetic and real-life M-ary
frequency shift keying (MFSK) signals. It is shown that the IF estimator from
the enhanced plots performs better than smoothed pseudo Wigner-Ville distribution
(SPWVD) and reassignment post-processing-based TFDs in terms
of mainlobe width (MLW) and variance, respectively, even at low signal-tonoise
ratio (SNR). On the other hand, time-frequency characterization of continuous
wave linear frequency modulation (CW-LFM) and pulse linear FM
(PLFM) radar signals are also investigated.
