Motion Compensation/Autofocus in Airborne Synthetic Aperture Radar: A Review

Chen, Jianlai; Xing, Mengdao; Yu, Hanwen; Liang, Buge; Peng, Jian; Sun, Guang-Cai

doi:10.1109/mgrs.2021.3113982

Cited by 89 publications

(31 citation statements)

References 95 publications

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“…The proposed parameter-adjusting method is different with the motion compensation (MoCo). The mechanism of MoCo is to use the trajectory data to compensate the echo collected by the non-ideal trajectory into the echo under the ideal trajectory [42,43]. Thus, the function of MoCo is to correct the influence of non-ideal trajectory on the echoed data.…”

Section: Discussionmentioning

confidence: 99%

“…In practical applications, the non-ideal trajectory and the precise of parameter adjustment will affect the imaging effect of the proposed algorithm. The non-ideal trajectory is caused by the motion error of the sensor [42,43]. In this section, the non-ideal trajectory is to be discussed from the motion error that can be accurately measured and the motion error that cannot be accurately measured.…”

Section: Discussionmentioning

confidence: 99%

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Multi-Layer Overlapped Subaperture Algorithm for Extremely-High-Squint High-Resolution Wide-Swath SAR Imaging with Continuously Time-Varying Radar Parameters

et al. 2022

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Extremely-high-squint (EHS) geometry of the traditional constant-parameter synthetic aperture radar (SAR) induces non-orthogonal wavenumber spectrum and hence the distortion of point spread function (PSF) in focused images. The method invented to overcome this problem is referred to as new-concept parameter-adjusting SAR. It corrects the PSF distortion by adjusting radar parameters, such as carrier frequency and chirp rate, based on instant data acquisition geometry. In this case, the characteristic of signal is quite different from the constant-parameter SAR and therefore, the traditional imaging algorithms cannot be directly applied for parameter-adjusting SAR imaging. However, the existing imaging algorithm for EHS parameter-adjusting SAR suffers from insufficient accuracy if a high-resolution wide-swath (HRWS) performance is required. Thus, this paper proposes a multi-layer overlapped subaperture algorithm (ML-OSA) for EHS HRWS parameter-adjusting SAR imaging with three main contributions: First, a more accurate signal model with time-varying radar parameters in high-squint geometry is derived. Second, phase errors are compensated with much higher accuracy by implementing multiple layers of coarse-to-fine spatially variant filters. Third, the analytical swath limit of the ML-OSA is derived by considering both the residual errors of signal model and phase compensations. The presented approach is validated via both the point- and extended-target computer simulations.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Multi-Layer Overlapped Subaperture Algorithm for Extremely-High-Squint High-Resolution Wide-Swath SAR Imaging with Continuously Time-Varying Radar Parameters

et al. 2022

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“…OFDM chirp waveform set has relatively high cross-correlation function peaks [34]. If its cross-correlation peaks are reduced to a certain extent, the OFDM chirp waveform can be applied to not only MIMO synthetic aperture radar (SAR) systems [35,36], but also other MIMO radar systems [37]. Compared with LFM signals, non-linear frequency modulation (NLFM) signals have a lower auto-correlation sidelobe and a larger degree of freedom.…”

Section: Signal M Echomentioning

confidence: 99%

Low Correlation Interference OFDM-NLFM Waveform Design for MIMO Radar Based on Alternating Optimization

Liu

Sun

et al. 2021

Sensors

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The OFDM chirp signal is suitable for MIMO radar applications due to its large time-bandwidth product, constant time-domain, and almost constant frequency-domain modulus. Particularly, by introducing the time-frequency structure of the non-linear frequency modulation (NLFM) signal into the design of an OFDM chirp waveform, a new OFDM-NLFM waveform with low peak auto-correlation sidelobe ratio (PASR) and peak cross-correlation ratio (PCCR) is obtained. IN-OFDM is the OFDM-NLFM waveform set currently with the lowest PASR and PCCR. Here we construct the optimization model of the OFDM-NLFM waveform set with the objective function being the maximum of the PASR and PCCR. Further, this paper proposes an OFDM-NLFM waveform set design algorithm inspired by alternating optimization. We implement the proposed algorithm by the alternate execution of two sub-algorithms. First, we keep both the sub-chirp sequence code matrix and sub-chirp rate plus and minus (PM) code matrix unchanged and use the particle swarm optimization (PSO) algorithm to obtain the optimal parameters of the NLFM signal’s time-frequency structure (NLFM parameters). Next, we keep current optimal NLFM parameters unchanged, and optimize the sub-chirp sequence code matrix and sub-chirp rate PM code matrix using the block coordinate descent (BCD) algorithm. The above two sub-algorithms are alternately executed until the objective function converges to the optimal solution. The results show that the PASR and PCCR of the obtained OFDM-NLFM waveform set are about 5 dB lower than that of the IN-OFDM.

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“…Synthetic aperture radar (SAR) is an active earth observation system with high resolution. The scene imaging quality is considerable with optical images; these images are increasingly important for scientific applications [1][2][3][4]. With the increasing improvement of SAR data collection capability and imaging algorithms, the research on interpreting high-resolution SAR images has received extensive attention, such as target detection and change detection [5,6].…”

Section: Introductionmentioning

confidence: 99%

SAR Target Detection Based on Improved SSD with Saliency Map and Residual Network

Fang

Gui

et al. 2022

Remote Sensing

Self Cite

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A target detection method based on an improved single shot multibox detector (SSD) is proposed to solve insufficient training samples for synthetic aperture radar (SAR) target detection. We propose two strategies to improve the SSD: model structure optimization and small sample augmentation. For model structure optimization, the first approach is to extract deep features of the target with residual networks instead of with VGGNet. Then, the aspect ratios of the default boxes are redesigned to match the different targets’ sizes. For small sample augmentation, besides the routine image processing methods, such as rotating, translating, and mirroring, enough training samples are obtained based on the saliency map theory in machine vision. Lastly, a simulated SAR image dataset called Geometric Objects (GO) is constructed, which contains dihedral angles, surface plates and cylinders. The experimental results on the GO-simulated image dataset and the MSTAR real image dataset demonstrate that the proposed method has better performance in SAR target detection than other detection methods.

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Motion Compensation/Autofocus in Airborne Synthetic Aperture Radar: A Review

Cited by 89 publications

References 95 publications

Multi-Layer Overlapped Subaperture Algorithm for Extremely-High-Squint High-Resolution Wide-Swath SAR Imaging with Continuously Time-Varying Radar Parameters

Multi-Layer Overlapped Subaperture Algorithm for Extremely-High-Squint High-Resolution Wide-Swath SAR Imaging with Continuously Time-Varying Radar Parameters

Low Correlation Interference OFDM-NLFM Waveform Design for MIMO Radar Based on Alternating Optimization

SAR Target Detection Based on Improved SSD with Saliency Map and Residual Network

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