Three-dimensional sparse recovery space-time adaptive processing for airborne radar

Three-dimensional sparse recovery space-time adaptive processing for airborne radar

Conventional 3-dimensional (3D) space-time adaptive processing (STAP) has achieved good performance for non-stationary clutter suppression. However, the performance of 3D STAP rapidly degrades when applied to heterogeneous clutter environment due to the requirement of a large number of independent a...

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Bibliographic Details
Main Authors: Keqing Duan, Hong Xu, Huadong Yuan, Wenchong Xie, Yongliang Wang
Format: Article
Language:English
Published: Wiley 2019-08-01
Series:The Journal of Engineering
Subjects:
radar clutter
radar signal processing
airborne radar
covariance matrices
space-time adaptive processing
learning (artificial intelligence)
Bayes methods
radar detection
dimensional sparse recovery space-time adaptive processing
3-dimensional space-time adaptive processing
nonstationary clutter suppression
heterogeneous clutter environment
independent distributed training samples
identically distributed training samples
clutter covariance matrix
efficient sparse recovery algorithm
multiple sparse Bayesian learning
conventional 2D sparsity-based STAP
3D clutter spectrum
Online Access:https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0343

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https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0343

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