scholarly journals An Intelligent Sample Selection Method for Space-Time Adaptive Processing in Heterogeneous Environment

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 30321-30330 ◽  
Author(s):  
Chongdi Duan ◽  
Yu Li ◽  
Wei Wei Wang
Keyword(s):  
Sample Selection ◽  
Selection Method ◽  
Space Time ◽  
Adaptive Processing ◽  
Heterogeneous Environment ◽  
Space Time Adaptive Processing

A generalized sample weighting method in heterogeneous environment for space-time adaptive processing

2018 ◽  
Vol 72 ◽  
pp. 147-159
Author(s):  
Huajian Xu ◽  
Zhiwei Yang ◽  
Shun He ◽  
Min Tian ◽  
Guisheng Liao ◽  
...  
Keyword(s):  
Space Time ◽  
Adaptive Processing ◽  
Heterogeneous Environment ◽  
Weighting Method ◽  
Sample Weighting ◽  
Space Time Adaptive Processing

scholarly journals Clutter Subspace Characteristics-Aided Space-Time Adaptive Outlier Sample Selection Method

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3108
Author(s):  
Dongning Fu ◽  
Guisheng Liao ◽  
Jingwei Xu
Keyword(s):  
Computational Complexity ◽  
Sample Selection ◽  
Critical Issue ◽  
Selection Method ◽  
Space Time ◽  
Inner Product ◽  
Airborne Radar ◽  
Specific Distribution ◽  
Training Samples ◽  
Space Time Adaptive Processing

For statistic space-time adaptive processing (STAP), a critical issue is estimating the clutter covariance matrix (CCM). However, sufficient training samples are difficult to obtain that satisfy the independent and identically distributed (IID) condition. It is because of the realistic heterogeneous environment faced by airborne radar. Moreover, one should eliminate contaminated training samples before CCM estimation. Aiming at the problems of the computational complexity and susceptibility to the outlier of the traditional generalized inner product (GIP) method, a clutter subspace-based training sampling selecting method is proposed combined with specific distribution in the space-time plane of clutter spectrum. Theoretical analysis and simulation results verified the proposed method and indicate that the proposed method is easy to construct CCM and has lower computational complexity and sensitivity to outliers.

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