MIMO Radar and Ground-Based SAR Imaging Systems: Equivalent Approaches for Remote Sensing

Tarchi, D.; Oliveri, Franco; Sammartino, P.F.

doi:10.1109/tgrs.2012.2199120

Cited by 122 publications

(81 citation statements)

References 19 publications

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“…In other words, the MIMO radar array is a non-collinear array. This height difference does not affect the imaging performance of far-field targets after calibration and thus can be ignored, which has been verified in [18] by an experiment using an array with 0.3 m height difference. For near-field targets, the effect is not obvious either if the azimuth angle and elevation angle of the scene are small enough.…”

Section: Introductionmentioning

confidence: 73%

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Design and Imaging of Ground-Based Multiple-Input Multiple-Output Synthetic Aperture Radar (MIMO SAR) with Non-Collinear Arrays

Wang

Tian

et al. 2017

Preprint

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Abstract:Multiple-Input Multiple-Output (MIMO) radar provides much more flexibility than the traditional radar thanks to its ability to realize far more observation channels than the actual number of transmit and receive (T/R) elements. In designing the MIMO imaging radar arrays, the commonly used virtual array theory generally assumes that all elements are on the same line. However, due to the physical size of the antennas and coupling effect between T/R elements, a certain height difference between T/R arrays is essential, which will result in the defocusing of edge points of the scene. On the other hand, the virtual array theory implies far-field approximation. Therefore, with a MIMO array designed by this theory, there will exist inevitable high grating lobes in the imaging results of near-field edge points of the scene. To tackle these problems, this paper derives the relationship between target's point spread function (PSF) and pattern of T/R arrays, by which the design criterion is presented for near-field imaging MIMO arrays. Firstly, the proper height between T/R arrays is designed to focus the near-field edge points well. Secondly, the far-field array is modified to suppress the grating lobes in the near-field area. Finally, the validity of the proposed methods is verified by two simulations and an experiment.

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Section: Introductionmentioning

confidence: 73%

“…Moreover, this key advantage provides a basis for vibration measurements. Therefore, researchers in many different fields have been attracted by MIMO radar imaging technology [13][14][15][16][17][18], such as security, medical imaging, slope monitoring [18][19][20], vehicle orientation estimation [21] and through-wall imaging [22].…”

Section: Introductionmentioning

confidence: 99%

Design and Imaging of Ground-Based Multiple-Input Multiple-Output Synthetic Aperture Radar (MIMO SAR) with Non-Collinear Arrays

Wang

Tian

et al. 2017

Preprint

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“…However, Multiple-Input-Multiple-Output (MIMO) technique presents an outstanding solution by forming a large virtue array simultaneously through signal processing with only few transmit and receive channels. Therefore, high resolution MIMO imaging systems are rapidly developed in current surveillance and security territories [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, the spectrum relation in MIMO structure is well interpreted with the wave propagation process. This relation is used to design imaging algorithm for the general cases of sparse linear MIMO array, which consists of dense and sparse arrays separated from each other, as in [2,3]. The next paragraphs are organized as following: Section 2 provides the spectrum relation in MIMO structure, and imaging algorithm for sparse linear MIMO array is proposed in Section 3.…”

Section: Introductionmentioning

confidence: 99%

Spectrum Relation of Linear Mimo Structure in Short Range Imaging

Shang¹,

Yang²,

Li³

2018

PIER Letters

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Abstract-This paper discusses the spectrum relation in Multiple-Input-Multiple-Output (MIMO) structures and presents an imaging algorithm for sparse linear MIMO array for short range imaging. This algorithm is available for MIMO array consisted by transmit and receive arrays separated from each other. The wave propagation process is used to interpret the spectrum relation in linear MIMO structures; therefore, the convolution relation of spectrum can be clearly understood. Moreover, the spectrum shift effect in linear MIMO structure with separated transmit and receive arrays is discussed and solved according to the spectrum relation. Above all, the imaging algorithm for sparse linear MIMO structure is presented, and the image performance is demonstrated by simulation results.

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“…In recent years, multi-input multi-output (MIMO) radar has drawn more and more attention [3,4,[6][7][8][9]11,[14][15][16][17][18][19][20][21]23,24,26,28,[33][34][35][36][37][38]40,41]. Different from traditional phased-array radar that transmits coherent waveforms, MIMO radar transmits arbitrary waveforms with multiple transmitting elements, which thereby leads to the so-called waveform diversity [6].…”

Section: Introductionmentioning

confidence: 99%

Robust Waveform Design for MIMO Radar with Imperfect Prior Knowledge

Wang

Pei

2015

Circuits Syst Signal Process

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Waveform optimization for multi-input multi-output radar usually depends on the initial parameter estimates (i.e., some prior information on the target of interest and scenario). However, it is sensitive to estimate errors and uncertainty in the parameters. Robust waveform design attempts to systematically alleviate the sensitivity by explicitly incorporating a parameter uncertainty model into the optimization problem. In this paper, we consider the robust waveform optimization to improve the worstcase performance of parameter estimation over a convex uncertainty model, which is based on the Cramer-Rao bound. An iterative algorithm is proposed to optimize the waveform covariance matrix such that the worst-case performance can be improved. Each iteration step in the proposed algorithm is solved by resorting to convex relaxation that belongs to the semidefinite programming class. Numerical results show that the worst-case performance can be improved considerably by the proposed method compared to that of uncorrelated waveforms and the non-robust method.

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MIMO Radar and Ground-Based SAR Imaging Systems: Equivalent Approaches for Remote Sensing

Cited by 122 publications

References 19 publications

Design and Imaging of Ground-Based Multiple-Input Multiple-Output Synthetic Aperture Radar (MIMO SAR) with Non-Collinear Arrays

Design and Imaging of Ground-Based Multiple-Input Multiple-Output Synthetic Aperture Radar (MIMO SAR) with Non-Collinear Arrays

Spectrum Relation of Linear Mimo Structure in Short Range Imaging

Robust Waveform Design for MIMO Radar with Imperfect Prior Knowledge

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