A Two-Stage Space-Time Adaptive Processing Method for MIMO Radar Based on Sparse Reconstruction

Frequenz ◽  
2017 ◽  
Vol 71 (11-12) ◽  
Author(s):  
Hanwei Liu ◽  
Yongshun Zhang ◽  
Yiduo Guo ◽  
Qiang Wang
Keyword(s):  
Matching Pursuit ◽  
Mimo Radar ◽  
Sparse Reconstruction ◽  
Clutter Suppression ◽  
Two Stage ◽  
Output Data ◽  
Reduced Dimension ◽  
Temporal And Spatial ◽  
Space Time Adaptive Processing ◽  
Complementary Subspace

AbstractTo effectively suppress clutter and blocking interference for MIMO radar, a two-stage STAP method based on sparse reconstruction is proposed. As interference is sparse in spatial domain, the subspace of it is estimated with only one snapshot by using Orthogonal Matching Pursuit (OMP) algorithm, and the array data is projected onto the complementary subspace of interference. In the sequel, matched-filtering is applied to the output data followed by clutter suppression with temporal and spatial freedom. The clutter suppression is utilized directly to reduced-dimension STAP (RD-STAP) algorithms. Simulation results demonstrate that the proposed method outperforms traditional methods and reduces sample requirement.

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.

A Space Time Autoaggressive Method Based on Parameters Estimation for Airborne MIMO Radar

2014 ◽  
Vol 556-562 ◽  
pp. 4496-4500
Author(s):  
Xing Hui Chen ◽  
Shi Qiao Gao
Keyword(s):  
Estimation Method ◽  
Mimo Radar ◽  
Space Time ◽  
Parameters Estimation ◽  
Ar Model ◽  
Model Parameters ◽  
Clutter Suppression ◽  
Training Samples ◽  
Multiple Input ◽  
Space Time Adaptive Processing

The clutter distribution of an airborne multiple input and multiple output (MIMO) radar in non-homogeneous environment varies with ranges and samples in different range gates are not independent identically distributed vectors, so that the statistical space time adaptive processing (STAP) methods degrade heavily. A clutter suppression method for airborne MIMO radar in non-homogeneous environments is studied in this paper. Firstly, Space time autoaggressive (STAR) method is introduced to airborne MIMO radar for clutter suppression and then an AR model parameters estimation method for STAR is proposed to decrease the complexity of traditional method. Simulation results show the proposed method can estimate parameters exactly and rapidly with only few training samples and be fit for clutter suppression in non-homogeneous environments.

Three-dimensional reduced-dimension transformation for MIMO radar space–time adaptive processing

2011 ◽  
Vol 91 (8) ◽  
pp. 2121-2126 ◽  
Author(s):  
Cong Xiang ◽  
Da-Zheng Feng ◽  
Hui Lv ◽  
Jie He ◽  
Hong-Wei Liu
Keyword(s):  
Three Dimensional ◽  
Mimo Radar ◽  
Space Time ◽  
Adaptive Processing ◽  
Reduced Dimension ◽  
Space Time Adaptive Processing

scholarly journals Compressive Sensing-Based Bandwidth Stitching for Multichannel Microwave Radars

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 665
Author(s):  
Paul Berry ◽  
Ngoc Hung Nguyen ◽  
Hai-Tan Tran
Keyword(s):  
Compressive Sensing ◽  
Matching Pursuit ◽  
Target Recognition ◽  
Radar Data ◽  
Effective Bandwidth ◽  
Sparse Reconstruction ◽  
Range Resolution ◽  
Combining Data ◽  
Temporal And Spatial ◽  
High Range

The problem of obtaining high range resolution (HRR) profiles for non-cooperative target recognition by coherently combining data from narrowband radars was investigated using sparse reconstruction techniques. If the radars concerned operate within different frequency bands, then this process increases the overall effective bandwidth and consequently enhances resolution. The case of unknown range offsets occurring between the radars’ range profiles due to incorrect temporal and spatial synchronisation between the radars was considered, and the use of both pruned orthogonal matching pursuit and refined l 1 -norm regularisation solvers was explored to estimate the offsets between the radars’ channels so as to attain the necessary coherence for combining their data. The proposed techniques were demonstrated and compared using simulated radar data.

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