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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Veröffentlicht in:	Remote sensing (Basel, Switzerland) Jg. 14; H. 15; S. 3800
Hauptverfasser:	Zhang, Chenxi, Zhao, Huiliang, Chen, Wenchao, Chen, Bo, Wang, Penghui, Jia, Changrui, Liu, Hongwei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.08.2022
Schlagworte:	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-Zugang:	Volltext
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