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
Published in:	Remote sensing (Basel, Switzerland) Vol. 14; no. 15; p. 3800
Main Authors:	Zhang, Chenxi, Zhao, Huiliang, Chen, Wenchao, Chen, Bo, Wang, Penghui, Jia, Changrui, Liu, Hongwei
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.08.2022
Subjects:	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
Online Access:	Get full text
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