Space–time adaptive processing and motion parameter estimation in multistatic passive radar using sparse Bayesian learning
Conventional space-time adaptive processing suffers from the requirement of a large
number of secondary samples. In this paper, a novel method is proposed to accurately …
number of secondary samples. In this paper, a novel method is proposed to accurately …
A novel sparse recovery‐based space‐time adaptive processing algorithm based on gridless sparse Bayesian learning for non‐sidelooking airborne radar
W Cui, T Wang, D Wang… - IET Radar, Sonar & …, 2023 - Wiley Online Library
Non‐sidelooking airborne radar encounters significant non‐stationary and heterogeneous
clutter environments, resulting in a severe shortage of samples. Sparse recovery‐based …
clutter environments, resulting in a severe shortage of samples. Sparse recovery‐based …
Space-time adaptive processing in bistatic passive radar exploiting complex Bayesian learning
In this paper, we develop a new space-time adaptive processing (STAP) technique for
bistatic passive radar by exploiting clutter sparsity so as to enable effective clutter …
bistatic passive radar by exploiting clutter sparsity so as to enable effective clutter …
Sparse Bayesian learning-based space-time adaptive processing with off-grid self-calibration for airborne radar
Space-time adaptive processing (STAP) for airborne radar has recently been enriched
owing to the development of methods based on sparse recovery techniques. These methods …
owing to the development of methods based on sparse recovery techniques. These methods …
[HTML][HTML] An improved iterative reweighted STAP algorithm for airborne radar
W Cui, T Wang, D Wang, C Liu - Remote Sensing, 2022 - mdpi.com
In recent years, sparse recovery-based space-time adaptive processing (SR-STAP)
technique has exhibited excellent performance with insufficient samples. Sparse Bayesian …
technique has exhibited excellent performance with insufficient samples. Sparse Bayesian …
Clutter suppression algorithm based on fast converging sparse Bayesian learning for airborne radar
Z Wang, W Xie, K Duan, Y Wang - Signal Processing, 2017 - Elsevier
Adapting the space-time adaptive processing (STAP) filter with finite number of secondary
data is of particular interest for airborne phased-array radar clutter suppression. Sparse …
data is of particular interest for airborne phased-array radar clutter suppression. Sparse …
Airborne radar space time adaptive processing based on atomic norm minimization
W Feng, Y Guo, Y Zhang, J Gong - Signal Processing, 2018 - Elsevier
Existing sparse recovery based space-time adaptive processing (SR-STAP) methods
discretize the angle-Doppler plane to generate the space-time steering dictionary, which will …
discretize the angle-Doppler plane to generate the space-time steering dictionary, which will …
Reduced dimension STAP based on sparse recovery in heterogeneous clutter environments
W Zhang, R An, N He, Z He, H Li - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
For airborne-phased array radar systems, space-time adaptive processing (STAP) is
supposed to be a crucial technique for improving target detection performance in the strong …
supposed to be a crucial technique for improving target detection performance in the strong …
Sparse representation based algorithm for airborne radar in beam-space post-Doppler reduced-dimension space-time adaptive processing
Y Guo, G Liao, W Feng - IEEE Access, 2017 - ieeexplore.ieee.org
An efficient and training-sample-reducing space-time adaptive processing (STAP) algorithm
based on sparse representation for ground clutter suppression in airborne radar is proposed …
based on sparse representation for ground clutter suppression in airborne radar is proposed …
A clutter suppression algorithm via enhanced sparse Bayesian learning for airborne radar
D Wang, T Wang, W Cui, X Zhang - IEEE Sensors Journal, 2023 - ieeexplore.ieee.org
The traditional space–time adaptive processing (STAP) method based on sparse Bayesian
learning (SBL) has the problems of low computational efficiency and slow convergence …
learning (SBL) has the problems of low computational efficiency and slow convergence …
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