Connected spectrogram graph fitting and random optimization combined time frequency analysis

Bingcheng Li

doi:10.1117/12.2663957

14 June 2023 Connected spectrogram graph fitting and random optimization combined time frequency analysis

Bingcheng Li

Proceedings Volume 12535, Radar Sensor Technology XXVII; 125350A (2023) https://doi.org/10.1117/12.2663957
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Because of its simple approximate representation to nonlinear time variant frequency modulations, polynomial chirplet has been used in radar, gravity analysis, electronic warfare and acoustic signal processing. However, due to its high dimension parameter spaces, direct polynomial chirplet transform has extremely high computational cost. In addition, the discrete implementation of polynomial chirplet transform causes a limited parameter estimation accuracy which may not satisfy the requirement in real applications. In this paper, by combining a connected spectrogram graph fitting with random optimization, we develop a new technique to address these computational cost and parameter estimation accuracy issues. We first convert a high dimensional polynomial chirplet transform into a low dimensional spectrogram implementation which significantly reduces computational cost. We then introduce interpolation and random optimization methods to improve the parameter estimation accuracy.

Conference Presentation

Citation Download Citation

Bingcheng Li "Connected spectrogram graph fitting and random optimization combined time frequency analysis", Proc. SPIE 12535, Radar Sensor Technology XXVII, 125350A (14 June 2023); https://doi.org/10.1117/12.2663957

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