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 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.

Automated summary

This article presents an approximate Bayesian inference framework for joint time-frequency analysis of micromotion targets in complex environments, which constructs a sparse observation model and probabilistic graphical model with a Gamma-complex Gaussian prior to solve high-dimension problems, and effectively solves model parameters using SWVI to obtain better JTF features and RID images.