Multiple input multiple output radar imaging based on multidimensional linear equations and sparse signal recovery

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.

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

International Journal of Antennas and Propagation ◽

10.1155/2015/539510 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 4

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.

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A Novel Convex Optimization for Receive Antenna Selection

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.186.611 ◽

2011 ◽

Vol 186 ◽

pp. 611-615

Author(s):

Yong Wang ◽

Hui Li

Keyword(s):

Convex Optimization ◽

Antenna Selection ◽

Linear Equations ◽

Multiple Input Multiple Output ◽

Optimal Choice ◽

Mimo Channels ◽

Newton Step ◽

Multiple Input ◽

Input Multiple Output ◽

Receive Antenna

This paper proposes a new receive antenna selection algorithm based on the theory of convex optimization that improve the system performance over Rayleigh fading multiple-input multiple-output (MIMO) channels. The algorithm is based on approximated relaxed original optimization problem. The main effort in the approximated relaxed method is computing the Newton step for the centering problem, which consists of solving sets of linear equations constraints. The method produces not only a suboptimal choice of receive antennas, but also, a bound on how well the globally optimal choice does. The Monte-Carlo simulations show that the algorithm proposed can provide the performance very close to that of the optimal selection based on exhaustive search.

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DOA and Range Estimation for FDA-MIMO Radar with Sparse Bayesian Learning

Remote Sensing ◽

10.3390/rs13132553 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2553

Author(s):

Qi Liu ◽

Xianpeng Wang ◽

Mengxing Huang ◽

Xiang Lan ◽

Lu Sun

Keyword(s):

Bayesian Learning ◽

Multiple Input Multiple Output ◽

Mimo Radar ◽

Signal Recovery ◽

Sparse Bayesian Learning ◽

Localization Algorithms ◽

Multiple Input ◽

Dependent Component ◽

Input Multiple Output ◽

Grid Points

Due to grid division, the existing target localization algorithms based on sparse signal recovery for the frequency diverse array multiple-input multiple-output (FDA-MIMO) radar not only suffer from high computational complexity but also encounter significant estimation performance degradation caused by off-grid gaps. To tackle the aforementioned problems, an effective off-grid Sparse Bayesian Learning (SBL) method is proposed in this paper, which enables the calculation the direction of arrival (DOA) and range estimates. First of all, the angle-dependent component is split by reconstructing the received data and contributes to immediately extract rough DOA estimates with the root SBL algorithm, which, subsequently, are utilized to obtain the paired rough range estimates. Furthermore, a discrete grid is constructed by the rough DOA and range estimates, and the 2D-SBL model is proposed to optimize the rough DOA and range estimates. Moreover, the expectation-maximization (EM) algorithm is utilized to update the grid points iteratively to further eliminate the errors caused by the off-grid model. Finally, theoretical analyses and numerical simulations illustrate the effectiveness and superiority of the proposed method.

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