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Article

Robust Multiple-Measurement Sparsity-Aware STAP with Bayesian Variational Autoencoder

1
National Lab of Radar Signal Processing, Xidian University, Xi’an 710071, China
2
The 38th Research Institute of China Electronics Technology Corporation, Hefei 230088, China
*
Author to whom correspondence should be addressed.
Academic Editor: Andreas Reigber
Remote Sens. 2022, 14(15), 3800; https://doi.org/10.3390/rs14153800
Received: 13 June 2022 / Revised: 18 July 2022 / Accepted: 20 July 2022 / Published: 6 August 2022
(This article belongs to the Special Issue Radar High-Speed Target Detection, Tracking, Imaging and Recognition)
Due to the shortage of independent and identically distributed (i.i.d.) training samples, space−time adaptive processing (STAP) often suffers remarkable performance degradation in the heterogeneous clutter environment. Sparse recovery (SR) techniques have been introduced into STAP for the benefit of the drastically reduced training requirement, but they are incompletely robust for involving the tricky selection of hyper−parameters or the undesirable point estimation for parameters. Given this issue, we incorporate the Multiple−measurement Complex−valued Variational relevance vector machines (MCV) to model the space−time echoes and provide a Gibbs−sampling−based method to estimate posterior distributions of parameters accurately. However, the Gibbs sampler require quantities of iterations, as unattractive as traditional Bayesian type SR−STAP algorithms when the real−time processing is desired. To address this problem, we further develop the Bayesian Autoencoding MCV for STAP (BAMCV−STAP), which builds the generative model according to MCV and approximates posterior distributions of parameters with an inference network pre−trained off−line, to realize fast reconstruction of measurements. Experimental results on simulated and measured data demonstrate that BAMCV−STAP can achieve suboptimal clutter suppression in terms of the output signal to interference plus noise ratio (SINR) loss, as well as the attractive real−time processing property in terms of the convergence rate and computational loads. View Full-Text
Keywords: clutter suppression; variational autoencoder (VAE); space−time adaptive processing (STAP); clutter plus noise covariance matrix (CCM); sparse recovery (SR) clutter suppression; variational autoencoder (VAE); space−time adaptive processing (STAP); clutter plus noise covariance matrix (CCM); sparse recovery (SR)
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MDPI and ACS Style

Zhang, C.; Zhao, H.; Chen, W.; Chen, B.; Wang, P.; Jia, C.; Liu, H. Robust Multiple-Measurement Sparsity-Aware STAP with Bayesian Variational Autoencoder. Remote Sens. 2022, 14, 3800. https://doi.org/10.3390/rs14153800

AMA Style

Zhang C, Zhao H, Chen W, Chen B, Wang P, Jia C, Liu H. Robust Multiple-Measurement Sparsity-Aware STAP with Bayesian Variational Autoencoder. Remote Sensing. 2022; 14(15):3800. https://doi.org/10.3390/rs14153800

Chicago/Turabian Style

Zhang, Chenxi, Huiliang Zhao, Wenchao Chen, Bo Chen, Penghui Wang, Changrui Jia, and Hongwei Liu. 2022. "Robust Multiple-Measurement Sparsity-Aware STAP with Bayesian Variational Autoencoder" Remote Sensing 14, no. 15: 3800. https://doi.org/10.3390/rs14153800

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