Design of MIMO radar waveform covariance matrix for Clutter and Jamming suppression based on space time adaptive processing

2016 ◽  
Vol 121 ◽  
pp. 60-69 ◽  
Author(s):  
Bo Tang ◽  
Jun Li ◽  
Yu Zhang ◽  
Jun Tang
Keyword(s):  
Covariance Matrix ◽  
Mimo Radar ◽  
Space Time ◽  
Adaptive Processing ◽  
Space Time Adaptive Processing

scholarly journals Local Degree of Freedom of Clutter for Reduced-Dimension Space-Time Adaptive Processing with MIMO Radar

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Yiduo Guo ◽  
Jian Gong ◽  
Yu Xiao
Keyword(s):  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Space Time ◽  
Degree Of Freedom ◽  
Adaptive Processing ◽  
Local Degree ◽  
Input Multiple Output ◽  
Reduced Dimension ◽  
Dimension Space ◽  
Space Time Adaptive Processing

Degree of freedom (DOF) of clutter in the reduced-dimension (RD) domain, which is called local DOF (LDOF), is of great importance for RD MIMO-STAP (space-time adaptive processing for multiple-input multiple-output radar) algorithms. In this paper, the LDOF equivalence of different RD MIMO-STAP algorithms are firstly proved, and then a generalized LDOF estimation rule under different conditions is developed to estimate the clutter LDOF for MIMO radar effectively. The accuracy of the proposed rule is verified, and how to design RD MIMO-STAP processors under the guidance of the proposed rule is presented through numerical simulations.

Bistatic FDA-MIMO radar space-time adaptive processing

2019 ◽  
Vol 163 ◽  
pp. 201-212 ◽  
Author(s):  
Cai Wen ◽  
Changzheng Ma ◽  
Jinye Peng ◽  
Jianxin Wu
Keyword(s):  
Mimo Radar ◽  
Space Time ◽  
Adaptive Processing ◽  
Space Time Adaptive Processing

Space-Time Adaptive Processing and Jamming

2018 ◽  
pp. 38-62
Keyword(s):  
Signal Processing ◽  
Computational Complexity ◽  
Covariance Matrix ◽  
Basic Concept ◽  
Processing Technique ◽  
Space Time ◽  
Adaptive Processing ◽  
Signal Processing Technique ◽  
Space Time Adaptive Processing ◽  
Established Technique

Space-time adaptive processing (STAP) has been a well-established technique, whose basic concept and theory are first put forward by Brennan and Reed. However, it is difficult to implement in the practical system because of the computational complexity and the sample limitation for estimating the clutter covariance matrix. STAP is a modern signal processing technique that can improve target detectability in the presence of a strong clutter Klemm.

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