scholarly journals A Through-the-Wall Radar Imaging Method Based on a Realistic Model

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Tian Jin ◽  
Alexander Yarovoy
Keyword(s):  
Multiple Input Multiple Output ◽  
Image Formation ◽  
Real Data ◽  
Radar Imaging ◽  
Realistic Model ◽  
Imaging Method ◽  
Computationally Efficient ◽  
Localization Accuracy ◽  
Multiple Input ◽  
Input Multiple Output

An image focusing method based on a realistic model for a wall is proposed for through-the-wall radar imaging using a multiple-input multiple-output array. A technique to estimate the wall parameters (i.e., position, thickness, and permittivity) from the radar returns is developed and tested. The estimated wall properties are used in the developed penetrating image formation to form images. The penetrating image formation developed is computationally efficient to realize real-time imaging, which does not depend on refraction points. The through-the-wall imaging method is validated on simulated and real data. It is shown that the proposed method provides high localization accuracy of targets concealed behind walls.

scholarly journals Multitarget Direct Localization Using Block Sparse Bayesian Learning in Distributed MIMO Radar

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Bin Sun ◽  
Haowen Chen ◽  
Xizhang Wei ◽  
Xiang Li
Keyword(s):  
Sparse Representation ◽  
Bayesian Learning ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Sparse Bayesian Learning ◽  
Localization Accuracy ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Direct Localization ◽  
Correct Target

The target localization in distributed multiple-input multiple-output (MIMO) radar is a problem of great interest. This problem becomes more complicated for the case of multitarget where the measurement should be associated with the correct target. Sparse representation has been demonstrated to be a powerful framework for direct position determination (DPD) algorithms which avoid the association process. In this paper, we explore a novel sparsity-based DPD method to locate multiple targets using distributed MIMO radar. Since the sparse representation coefficients exhibit block sparsity, we use a block sparse Bayesian learning (BSBL) method to estimate the locations of multitarget, which has many advantages over existing block sparse model based algorithms. Experimental results illustrate that DPD using BSBL can achieve better localization accuracy and higher robustness against block coherence and compressed sensing (CS) than popular algorithms in most cases especially for dense targets case.

scholarly journals An Imaging Plane Calibration Method for MIMO Radar Imaging

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5261
Author(s):  
Yuanyue Guo ◽  
Bo Yuan ◽  
Zhaohui Wang ◽  
Rui Xia
Keyword(s):  
Multiple Input Multiple Output ◽  
Calibration Method ◽  
Radar Imaging ◽  
Spatial Spectrum ◽  
Mimo Radar ◽  
Cooperative Movement ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Calibration Algorithm ◽  
Imaging Plane

In two dimensional cross-range multiple-input multiple-output radar imaging for aerial targets, due to the non-cooperative movement of the targets, the estimated imaging plane parameters, namely the center and the posture angles of the imaging plane, may have deviations from true values, which defocus the final image. This problem is called imaging plane mismatch in this paper. Focusing on this problem, firstly the deviations of spatial spectrum fulfilling region caused by imaging plane mismatch is analyzed, as well as the errors of the corresponding spatial spectral values. Thereupon, the calibration operation is deduced when the imaging plane parameters are accurately obtained. Afterwards, an imaging plane calibration algorithm is proposed to utilize particle swarm optimization to search out the imaging plane parameters. Finally, it is demonstrated through simulations that the proposed algorithm can accurately estimate the imaging plane parameters and achieve good image focusing performance.

scholarly journals Comparison of 10–18 GHz SAR and MIMO-based short-range imaging radars

2010 ◽  
Vol 2 (3-4) ◽  
pp. 369-377 ◽  
Author(s):  
Timofey Savelyev ◽  
Xiaodong Zhuge ◽  
Bill Yang ◽  
Pascal Aubry ◽  
Alexander Yarovoy ◽  
...  
Keyword(s):  
Short Range ◽  
Multiple Input Multiple Output ◽  
Vertical Plane ◽  
Ultra Wideband ◽  
Imaging Method ◽  
Digital Beamforming ◽  
Mimo Antenna ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Concealed Weapon Detection

This paper presents an experimental investigation of two approaches to short-range radar imaging at microwaves by means of ultra-wideband (UWB) technology. The first approach represents a classical synthetic aperture radar (SAR) that employs a transmit–receive antenna pair on mechanical scanner. The second one makes use of a multiple input multiple output (MIMO) antenna array that scans electronically in the horizontal plane and mechanically, installed on the scanner, in the vertical plane. The mechanical scanning in only one direction reduces significantly the measurement time. Two respective prototypes have been built and compared. Both systems comprise the same 10–18 GHz antennas and multi-channel video impulse electronics while the same data processing and imaging method based on Kirchhoff migration is applied to acquired data for digital beamforming. The study has been done for an application of concealed weapon detection.

scholarly journals Computationally Efficient Unitary ESPRIT Algorithm in Bistatic MIMO Radar

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Baobao Liu ◽  
Tao Xue ◽  
Cong Xu ◽  
Yongjun Liu
Keyword(s):  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Mimo Radar ◽  
Computational Effort ◽  
Estimation Accuracy ◽  
Computationally Efficient ◽  
Angle Estimation ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Esprit Algorithm

A low complexity unitary estimating signal parameter via rotational invariance techniques (ESPRIT) algorithm is presented for angle estimation in bistatic multiple-input-multiple-output (MIMO) radar. The devised algorithm only requires calculating two submatrices covariance matrix, which reduces the computation cost in comparison with subspace methods. Moreover, the signal subspace can be efficiently acquired by exploiting the NystrÖm method, which only needs O M N K 2 flops. Thus, the presented algorithm has an essentially diminished computational effort, especially useful when K ≪ M N , while it can achieve efficient angle estimation accuracy as well as the existing algorithms. Several theoretical analysis and simulation results are provided to demonstrate the usefulness of the proposed scheme.

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