Fast Inverse-Scattering Reconstruction for Airborne High-Squint Radar Imagery Based on Doppler Centroid Compensation

Luo, Jiawei; Li, Jie; Mao, Deqing; Zhang, Yin; Huang, Yulin; Yang, Jianyu

doi:10.1109/tgrs.2021.3069499

Cited by 33 publications

(15 citation statements)

References 55 publications

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“…2) Then, the platform-to-ground distances of all grids within the coverage of the current search beam position are calculated by Eq. (4)(5)(6). 3) The clutter-free detection region of the current beam position is obtained by searching for the smallest element in R i .…”

Section: A Summary Of the Proposed Strategymentioning

confidence: 99%

“…I N modern electronic countermeasures, the advancement and integration of various information technologies have led to the rapid development of airborne phased array radar (APAR) [1][2][3][4][5][6][7]. Compared with traditional mechanical scanning radar systems, the electronically controlled array antenna used in APAR systems provides strong beam agility and beam control capabilities, which is significant for the primary task of APAR: the fast and accurate airspace search [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Search Strategy of Low-Altitude Target for Airborne Phased Array Radar Using Digital Elevation Model

Luo

Huang

Zhang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The search for low-altitude targets becomes a more challenging task for airborne phased array radar, due to the limited system resources and serious ground clutter. In this paper, an optimal search strategy using the digital elevation model (DEM) is proposed to allow for fast and high-probability search for low-altitude targets. First, a clutter-free detection region is formed by introducing the DEM. Compared with the traditional methods, the detection probability of the lowaltitude targets can be improved. Second, the search problem is transformed into a constrained multi-objective optimization problem. The proposed strategy can simultaneously minimize the search time and maximize the target detection probability. Finally, two intelligent evolutionary algorithms are compared to verify the proposed strategy, and the proposed strategy is also testified by utilizing a real DEM data of Dujiangyan, Sichuan, China. Simulated results indicate that the proposed optimal search framework improves the detection probability and reduces time cost for low-altitude targets.

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Section: A Summary Of the Proposed Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Search Strategy of Low-Altitude Target for Airborne Phased Array Radar Using Digital Elevation Model

Luo

Huang

Zhang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…S PACEBORNE-airborne multistatic synthetic aperture radar (SA-MuSAR) system can provide forward-looking capability for receivers because it has the potential of flexible geometric configuration (GC) [1]- [4]. However, to obtain a high-quality fused image with limited observation time, the GC among the transmitter and receivers should be optimally designed, which essentially determines the quality of reconstructed images [5]- [7].…”

Section: Introductionmentioning

confidence: 99%

Forward-Looking Geometric Configuration Optimization Design for Spaceborne-Airborne Multistatic Synthetic Aperture Radar

Mao

Pei

Huo

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Spaceborne-airborne multistatic synthetic aperture radar (SA-MuSAR) has the ability to provide high-resolution forward-looking imagery for receivers, but it relies on careful design of the geometric configuration (GC). In this paper, a forward-looking GC optimization design method is proposed to obtain a high-quality fused image with limited observation time. First, the relationship between the spatial resolution and GC is illustrated by the wavenumber spectrum distribution of SA-MuSAR. Second, GC evaluators depending on the distribution of multiple wavenumber spectrum data are proposed. The GC design problem of coherent SA-MuSAR is transformed into a constrained multi-objective optimization problem (CMOP). An intelligent evolutionary algorithm is adopted to optimize the wavenumber spectrum distribution. With the proposed method, high-quality forward-looking imagery can be obtained with a short observation time. Numerical simulations are carried out to verify the effectiveness of the proposed method.

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“…Radar has been widely used in military and civilian fields thanks to its all-day and all-weather imaging capabilities. Scanning radar, as a simple radar device, can obtain target information in the imaging area only by antenna scanning [1][2][3]. Scanning radar has no imaging blind-zone and can achieve forward-looking imaging [4,5], which is of great significance for the precision guidance of weapons, automatic driving of vehicles and airdropping of materials.…”

Section: Introductionmentioning

confidence: 99%

A Sparse Denoising-Based Super-Resolution Method for Scanning Radar Imaging

Zhang

Huang

et al. 2021

Remote Sensing

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Scanning radar enables wide-range imaging through antenna scanning and is widely used for radar warning. The Rayleigh criterion indicates that narrow beams of radar are required to improve the azimuth resolution. However, a narrower beam means a larger antenna aperture. In practical applications, due to platform limitations, the antenna aperture is limited, resulting in a low azimuth resolution. The conventional sparse super-resolution method (SSM) has been proposed for improving the azimuth resolution of scanning radar imaging and achieving superior performance. This method uses the L1 norm to represent the sparse prior of the target and solves the L1 regularization problem to achieve super-resolution imaging under the regularization framework. The resolution of strong-point targets is improved efficiently. However, for some targets with typical shapes, the strong sparsity of the L1 norm treats them as strong-point targets, resulting in the loss of shape characteristics. Thus, we can only see the strong points in its processing results. However, in some applications that need to identify targets in detail, SSM can lead to false judgments. In this paper, a sparse denoising-based super-resolution method (SDBSM) is proposed to compensate for the deficiency of traditional SSM. The proposed SDBSM uses a sparse minimization scheme for denoising, which helps to reduce the influence of noise. Then, the super-resolution imaging is achieved by alternating iterative denoising and deconvolution. As the proposed SDBSM uses the L1 norm for denoising rather than deconvolution, the strong sparsity constraint of the L1 norm is reduced. Therefore, it can effectively preserve the shape of the target while improving the azimuth resolution. The performance of the proposed SDBSM was demonstrated via simulation and real data processing results.

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Fast Inverse-Scattering Reconstruction for Airborne High-Squint Radar Imagery Based on Doppler Centroid Compensation

Cited by 33 publications

References 55 publications

Optimal Search Strategy of Low-Altitude Target for Airborne Phased Array Radar Using Digital Elevation Model

Optimal Search Strategy of Low-Altitude Target for Airborne Phased Array Radar Using Digital Elevation Model

Forward-Looking Geometric Configuration Optimization Design for Spaceborne-Airborne Multistatic Synthetic Aperture Radar

A Sparse Denoising-Based Super-Resolution Method for Scanning Radar Imaging

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