Passive SAR imaging using DVB‐T illumination for airborne applications

Gromek, Damian; Radecki, Krzysztof; Drozdowicz, Jędrzej; Samczyński, Piotr; Szabatin, J.

doi:10.1049/iet-rsn.2018.5123

Cited by 18 publications

(9 citation statements)

References 18 publications

(24 reference statements)

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“…Eq. (11) shows that after range compression, there are ambiguous peaks due to the presence of pilot signals, which are separated in time by /12 U kT . Because pilot locations are constant in each DVB-T symbol, we may then expect that the ambiguous range-compressed peaks will result in ambiguous range histories every /12 U kT s ( Fig.…”

Section: A Rc Processingmentioning

confidence: 99%

Improved Passive SAR Imaging With DVB-T Transmissions

Fang

Atkinson

Sayin

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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Section: A Rc Processingmentioning

confidence: 99%

Improved Passive SAR Imaging With DVB-T Transmissions

Fang

Atkinson

Sayin

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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“…The first part is the range compressed signal, which can be calcualted by (8). The second part is a differentiation operation applied on the range compressed signal, which is a convolution result, shown in (7) previously. This differentiation can achieved by two ways during the program implementation, listed as follows,…”

Section: B Proposed Algorithm For Range Compression Improvementmentioning

confidence: 99%

An Image Formation Algorithm for Bistatic SAR Using GNSS Signal With Improved Range Resolution

et al. 2020

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Passive synthetic aperture radar (SAR) using Global Navigation Satellite System (GNSS) signals as the illuminators of opportunity has a relative coarse range resolution due to the narrow bandwidth of the navigation signals and the bi-static acquisition geometry, limiting its application for high-resolution requirement. In this paper, an image formation algorithm for range resolution improvement is investigated. A modified second order differentiation based method is applied on the range compressed signal of reference channel and surveillance channel, improving range resolution and keeping side-lobes at a lower level simultaneously, and the phase of processed signal is preserved by phase multiplication compensation for the following azimuth compression processing. To validate the effectiveness and benefits of the proposed algorithm in terms of range resolution and targets side-lobes, point targets simulation under different situations and real field experiment with strong scatters are conducted and discussed. Compared with the traditional GNSS-based passive SAR imaging algorithm, the results of the proposed method show that the imaging performance can be improved for better range resolution and lower side-lobes, benefitting for better distinguishing nearby targets and recognizing weak targets.

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“…It is worth mentioning that the value of s i is determined by two factors in Eq. (12). By maximizing the first term p(s i |s N i ), we can preserve the spatially clustered property of targets.…”

Section: Probabilistic Graph Modelmentioning

confidence: 99%

“…While many passive radar systems are focused on the detection of targets with stationary groundbased receivers, the passive radar systems mounted on the moving airborne platform have received substantial attention in recent years [5][6][7]. Airborne passive synthetic aperture radar (SAR) can allow two-dimensional images of the ground surface to be imaged while providing the advantage of an increased target detection range and a reduced terrain masking effect [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Multistatic Airborne Passive Synthetic Aperture Radar Imaging Based on Two-Level Block Sparsity

Qu¹,

Liu²

2019

PIER M

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Available multiple illuminators in a multistatic airborne passive synthetic aperture radar (SAR) system can enhance SAR imaging quality. In this paper, a new imaging algorithm based on twolevel block sparsity for a multistatic airborne passive SAR system is proposed. The proposed imaging algorithm named by two-level block matching pursuit (BMP) algorithm utilizes both the spatially clustered property of observed targets and joint sparsity of the multistatic observation, i.e., two-level block sparsity to achieve the imaging reconstruction of an observed scene. The simulation results show that the proposed two-level BMP imaging algorithm for the multistatic airborne passive SAR system can reduce imaging reconstruction time and provide enhanced imaging reconstruction quality compared to the state-of-the-art structured sparse imaging algorithm.

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Passive SAR imaging using DVB‐T illumination for airborne applications

Cited by 18 publications

References 18 publications

Improved Passive SAR Imaging With DVB-T Transmissions

Improved Passive SAR Imaging With DVB-T Transmissions

An Image Formation Algorithm for Bistatic SAR Using GNSS Signal With Improved Range Resolution

Multistatic Airborne Passive Synthetic Aperture Radar Imaging Based on Two-Level Block Sparsity

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