Minimum Redundancy Array—A Baseline Optimization Strategy for Urban SAR Tomography

Wei, Lianhuan; Feng, Qiuyue; Liu, Shanjun; Bignami, Christian; Tolomei, Cristiano; Zhao, Dong Ming

doi:10.3390/rs12183100

Cited by 8 publications

(3 citation statements)

References 52 publications

(107 reference statements)

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“…The slight drop in the detection rate around k s = 1 is caused by the interference of the two strong scatterers. The interference effect becomes stronger when the amplitude of the scatterers becomes closer [30]. As we can see, with a higher grid density, SL1MMER 30 performs slightly better than SL1MMER 20 .…”

Section: Location Accuracymentioning

confidence: 79%

SAR Tomography Based on Atomic Norm Minimization in Urban Areas

Liu

Peng

et al. 2022

Remote Sensing

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Synthetic aperture radar (SAR) tomography (TomoSAR) is a powerful tool for the three-dimensional (3D) reconstruction of buildings in urban areas. At present, the compressed sensing (CS) technique has been widely used in the TomoSAR inversion of urban areas because of the sparsity of the backscattering power of buildings along the elevation direction. However, this algorithm discretizes the elevation and assumes that the scatterers are located on predetermined finite grids. In fact, scatterers can lie anywhere in the elevation direction, regardless of grid point constraints. The phenomenon of scatterer positioning errors due to elevation discretization is called the off-grid effect, which will affect the height estimation accuracy of TomoSAR. To overcome this problem, we proposed a TomoSAR reconstruction algorithm based on atomic norm minimization (Tomo-ANM) in this paper. Tomo-ANM employs ANM, a continuous compressed sensing technique, to obtain scatterer positions on the continuous dictionary, thus eliminating the off-grid effect. Baseline compensation is necessary to obtain the data of virtual uniform baselines or the samples of uniform data during preprocessing. A fast realization of ANM, IVDST, is utilized to accelerate the process. Tomo-ANM was tested through simulation experiments, and the results confirmed the validity of eliminating the influence of off-grid effects and exhibited an improved location accuracy and detection rate in less time compared with the on-grid TomoSAR algorithm SL1MMER. Real data experiments based on eight staring spotlight TerraSAR-X images showed that Tomo-ANM can improve the accuracy of building height estimation by 4.83% relative to its real height.

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Section: Location Accuracymentioning

confidence: 79%

SAR Tomography Based on Atomic Norm Minimization in Urban Areas

Liu

Peng

et al. 2022

Remote Sensing

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“…For linear array, there will be no grating lobes as long as the antenna interval is half of the wavelength. Similar to the redundancy of equally spaced sampling in time domain [3], minimum-redundancy arrays [4][5][6], nested arrays [7], and co-prime arrays [8][9][10] have been proposed successively. However, the above arrays do not consider the prior information such as non-coherent processing, and only realize the array configuration optimization from the perspective of spatial snapshot.…”

Section: Introductionmentioning

confidence: 99%

Target Localization Based on High Resolution Mode of MIMO Radar with Widely Separated Antennas

Liu

et al. 2022

Remote Sensing

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Coherent processing of multiple-input multiple-output (MIMO) radar with widely separated antennas has high resolution capability, but it also brings ambiguity in target localization. In view of the ambiguity problem, different from other signal processing sub-directions such as array configuration optimization or continuity of phase in space/time, this paper analyzes it from the information level, that is, the tracking method is adopted. First, by using the state equation and measurement equation, the echo data of multiple coherent processing intervals (CPI) are collected to improve the target localization accuracy as much as possible. Second, the non-coherent joint probability data association filter (JPDAF) is used to achieve stable tracking of spatial cross targets without ambiguity measurements. Third, based on the tracking results of the non-coherent JPDAF, the ambiguity of coherent measurement is resolved, that is, the coherent JPDAF is realized. By means of non-coherent and coherent alternating JPDAF (NCCAF) algorithms, high accuracy localization of multiple targets is achieved. Finally, numerical simulations are carried out to validate the effectiveness of the proposed NCCAF algorithm.

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“…To capture better 3D TomoSAR or D-TomoSAR results for the next level of applications, many scholars have dedicated extensive research on TomoSAR for layover separation. Some focus on baseline optimization [7], [8], some mainly study effective extraction of PS points [9], and some are devoted to studies on TomoSAR imaging algorithms. In this paper, TomoSAR imaging methods are investigated in depth.…”

Section: Introductionmentioning

confidence: 99%

Spatial Shift Phenomenon Compensation for TomoSAR Imaging Using High-Resolution TerraSAR-X Data

et al. 2021

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Massive high-resolution spaceborne synthetic aperture radar (SAR) data are available nowadays, effecting rapid development in the urban remote sensing technology. In this study, we use a set of 27 scenes from TerraSAR-X to reconstruct the three-dimensional (3D) point clouds of a skyscraper-North Star Times Tower in Beijing, China. We study in detail the derivation process of a 3D model of tomographic SAR (TomoSAR) from the two-dimensional (2D) imaging mathematical model of SAR. We observe the presence of a spatial shift in the elevation direction of TomoSAR, which yields poor and even incorrect results when greedy algorithms such as orthogonal matching pursuit (OMP) or regularized orthogonal matching pursuit (ROMP) algorithms are applied. Then, we propose a phase compensation method to eliminate the spatial shift. Both OMP and ROMP algorithms are used for TomoSAR imaging on high-resolution TerraSAR-X data. The experimental results with North Star Times Tower demonstrate the accuracy of the theoretical analysis and the effectiveness of the algorithms. Finally, we compare the ROMP and OMP algorithms and conclude that both algorithms can yield similar reconstruction results in TomoSAR imaging. The ROMP algorithm does not depend on sparsity and can suppress noise through regularization selection at the cost of time. The OMP algorithm introduces noise when the sparsity is inaccurate.INDEX TERMS SAR tomography, regularized orthogonal match pursuit, spatial shift theory.

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Minimum Redundancy Array—A Baseline Optimization Strategy for Urban SAR Tomography

Cited by 8 publications

References 52 publications

SAR Tomography Based on Atomic Norm Minimization in Urban Areas

SAR Tomography Based on Atomic Norm Minimization in Urban Areas

Target Localization Based on High Resolution Mode of MIMO Radar with Widely Separated Antennas

Spatial Shift Phenomenon Compensation for TomoSAR Imaging Using High-Resolution TerraSAR-X Data

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