Time-frequency analysis of multi-component signal with dynamic births and deaths has always been one of the difficulties in non-stationary signal processing. In this paper, a random finite set approach to analyzing, detecting, and tracking multi-component signal is proposed. By means of time-frequency transform, such as short-time Fourier transform or iterative adaptive approach, and polynomial prediction models, time-frequency analysis of multi-component signal can be formulated as multi-target tracking with random finite sets. The analyses show that, by adopting the given initial weight assignment algorithm and the provided joint likelihood function of the amplitudes and frequencies of the time-frequency spectra, the Gaussian mixture probability hypothesis density filter can be used to achieve the analysis, detection and tracking of dynamic time-frequency spectra. Simulation results show that the proposed method effectively improves the tracking accuracy of dynamic time-frequency spectra, and its performances of weak component detection and close modes analytical capabilities are much better than the ones reported in the literature.
WANG Yuebin
,
JIANG Jingfei
,
ZHANG Jianqiu
. Random finite set approach to analyzing, detecting, and tracking dynamic time-frequency spectra[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2019
, 40(6)
: 322600
-322600
.
DOI: 10.7527/S1000-6893.2018.22600
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