JTF Analysis of Micromotion Targets Based on Single-Window Variational Inference

This article addresses the problem of joint time-frequency (JTF) analysis of micromotion targets in complex environments in an approximate Bayesian inference framework. First, the sparse observation model is constructed, which is then decomposed into a series of single-window-JTF (SW-JTF) analysis p...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2021-08, Vol.59 (8), p.6600-6608
Main Authors: Hui, Ye, Bai, Xueru, Zhou, Feng
Format: Article
Language:English
Subjects:
Analysis
Bayes methods
Bayesian analysis
Dictionaries
Dimensions
Doppler sonar
Frequency analysis
Image quality
Imaging
Joint time-frequency (JTF) analysis
low signal-to-noise ratio (SNR)
micromotion
Microsoft Windows
Narrowband
Probability theory
Radar
Scattering
Signal to noise ratio
sparse Bayesian inference
Statistical inference
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Summary:This article addresses the problem of joint time-frequency (JTF) analysis of micromotion targets in complex environments in an approximate Bayesian inference framework. First, the sparse observation model is constructed, which is then decomposed into a series of single-window-JTF (SW-JTF) analysis problems to tackle the high dimension of the over-complete dictionary. On this basis, the probabilistic graphical model is constructed by imposing the Gamma-complex Gaussian prior to the JTF distribution. Finally, the model parameters are solved effectively by single-window variational inference (SWVI). Compared with the available methods, the proposed method could obtain better-focused JTF signature for narrowband data and higher quality range-instantaneous Doppler (RID) image for wideband data, especially in low signal-to-noise ratio (SNR) and data corruption scenarios.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2020.3028142