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

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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Bibliographic Details
Main Authors: Chenxi Zhang, Huiliang Zhao, Wenchao Chen, Bo Chen, Penghui Wang, Changrui Jia, Hongwei Liu
Format: Article
Language:English
Published: MDPI AG 2022-08-01
Series:Remote Sensing
Subjects:
clutter suppression
variational autoencoder (VAE)
space−time adaptive processing (STAP)
clutter plus noise covariance matrix (CCM)
sparse recovery (SR)
Online Access:https://www.mdpi.com/2072-4292/14/15/3800

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