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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2019, Vol. 40 ›› Issue (6): 322600-322600.doi: 10.7527/S1000-6893.2018.22600

• Electronics and Electrical Engineering and Control • Previous Articles     Next Articles

Random finite set approach to analyzing, detecting, and tracking dynamic time-frequency spectra

WANG Yuebin1,2, JIANG Jingfei2, ZHANG Jianqiu1,2   

  1. 1. The Research Center of Smart Networks and Systems and Department of Electronic Engineering, Fudan University, Shanghai 200433, China;
    2. Science and Technology on Electronic Information and Control Laboratory, Chengdu 610036, China
  • Received:2018-08-13 Revised:2018-09-19 Online:2019-06-15 Published:2019-06-26
  • Supported by:
    National Natural Science Foundation of China (61571131); Science and Technology on Electronic Information and Control Laboratory Foundation

Abstract: 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.

Key words: time-frequency analysis, random finite set, polynomial prediction, multi-target tracking, Gaussian mixture probability hypothesis density filter

CLC Number: