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

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...

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Vydáno v:	Remote sensing (Basel, Switzerland) Ročník 14; číslo 15; s. 3800
Hlavní autoři:	Zhang, Chenxi, Zhao, Huiliang, Chen, Wenchao, Chen, Bo, Wang, Penghui, Jia, Changrui, Liu, Hongwei
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.08.2022
Témata:	Adaptive sampling Airborne radar Algorithms Bayesian analysis Bayesian theory Clutter clutter plus noise covariance matrix (CCM) clutter suppression Computer applications Dictionaries Echoes Machine learning Mathematical models Methods Parameters Performance degradation Remote sensing Robustness space and time Space-time adaptive processing space−time adaptive processing (STAP) sparse recovery (SR) Sparsity Training variational autoencoder (VAE)
ISSN:	2072-4292, 2072-4292
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Abstract	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.
AbstractList	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.
Author	Liu, Hongwei Zhao, Huiliang Chen, Wenchao Jia, Changrui Zhang, Chenxi Wang, Penghui Chen, Bo
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SubjectTerms	Adaptive sampling Airborne radar Algorithms Bayesian analysis Bayesian theory Clutter clutter plus noise covariance matrix (CCM) clutter suppression Computer applications Dictionaries Echoes Machine learning Mathematical models Methods Parameters Performance degradation Remote sensing Robustness space and time Space-time adaptive processing space−time adaptive processing (STAP) sparse recovery (SR) Sparsity Training variational autoencoder (VAE)
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Title	Robust Multiple-Measurement Sparsity-Aware STAP with Bayesian Variational Autoencoder
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