MIMO Radar Imaging With Nonorthogonal Waveforms Based on Joint-Block Sparse Recovery

2018 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaowei Hu ◽  
Ningning Tong ◽  
Yongshun Zhang ◽  
Darong Huang
Keyword(s):  
Radar Imaging ◽  
Sparse Recovery ◽  
Mimo Radar

Adaptive Waveform Optimization for MIMO Radar Imaging Based on Sparse Recovery

2020 ◽  
Vol 58 (4) ◽  
pp. 2898-2914 ◽  
Author(s):  
Xiaowei Hu ◽  
Cunqian Feng ◽  
Yuchen Wang ◽  
Yiduo Guo
Keyword(s):  
Radar Imaging ◽  
Sparse Recovery ◽  
Mimo Radar ◽  
Waveform Optimization

Adaptive Sparse Recovery of Moving Targets for Distributed MIMO Radar

2014 ◽  
Vol 933 ◽  
pp. 450-455
Author(s):  
Hui Yu ◽  
Guang Hua Lu ◽  
Hai Long Zhang
Keyword(s):  
High Resolution ◽  
Sparse Recovery ◽  
Mimo Radar ◽  
Moving Target ◽  
Recovery Algorithm ◽  
Low Snr ◽  
Computation Efficiency ◽  
Target Imaging ◽  
Moving Target Imaging ◽  
Recovery Error

The high resolution and better recovery performance with distributed MIMO radar would be significantly degraded when the target moves at an unknown velocity. In this paper, we propose an adaptive sparse recovery algorithm for moving target imaging to estimate the velocity and image jointly with high computation efficiency. With an iteration mechanism, the proposed method updates the image and estimates the velocity alternately by sequentially minimizing the norm and the recovery error. Numerical simulations are carried out to demonstrate that the proposed algorithm can retrieve high-resolution image and accurate velocity simultaneously even in low SNR.

scholarly journals An Accurate Sparse Recovery Algorithm for Range-Angle Localization of Targets via Double-Pulse FDA-MIMO Radar

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Qi Liu ◽  
Xianpeng Wang ◽  
Liangtian Wan ◽  
Mengxing Huang ◽  
Lu Sun
Keyword(s):  
Sparse Recovery ◽  
Mimo Radar ◽  
Double Pulse ◽  
Grid Refinement ◽  
Recovery Algorithm ◽  
Refinement Method ◽  
Overcomplete Dictionary ◽  
Atomic Correlation ◽  
Svd Method ◽  
Zero Frequency

In this paper, a sparse recovery algorithm based on a double-pulse FDA-MIMO radar is proposed to jointly extract the angle and range estimates of targets. Firstly, the angle estimates of targets are calculated by transmitting a pulse with a zero frequency increment and employing the improved l 1 -SVD method. Subsequently, the range estimates of targets are achieved by utilizing a pulse with a nonzero frequency increment. Specifically, after obtaining the angle estimates of targets, we perform dimensionality reduction processing on the overcomplete dictionary to achieve the automatically paired range and angle in range estimation. Grid partition will bring a heavy computational burden. Therefore, we adopt an iterative grid refinement method to alleviate the above limitation on parameter estimation and propose a new iteration criterion to improve the error between real parameters and their estimates to get a trade-off between the high-precision grid and the atomic correlation. Finally, the proposed algorithm is evaluated by providing the results of the Cramér-Rao lower bound (CRLB) and numerical root mean square error (RMSE).

Sparsity-based MIMO radar imaging method

2011 ◽  
Author(s):  
Dang-Wei Wang ◽  
A-Lei Chen ◽  
Jun- Quan Yuan ◽  
Xiao-Yan Ma
Keyword(s):  
Radar Imaging ◽  
Mimo Radar ◽  
Imaging Method
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