Noise-Robust Vibration Phase Compensation for Satellite ISAL Imaging by Frequency Descent Minimum Entropy Optimization

Wang, Xuan; Guo, Liang; Li, Yachao; Han, Liang; Xu, Qing; Jing, Dan; Li, Liangchao; Xing, Mengdao

doi:10.1109/tgrs.2022.3204077

Cited by 12 publications

(3 citation statements)

References 42 publications

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“…Due to the wavelength of optical synthetic aperture into radar being in the order of microns, the accuracy of phase error correction is extremely demanding. To address this, IE has been used as a fitness function for ISAL phase error estimation in several works 9 , 17 , 18 . The concentration of image energy distribution is directly proportional to the clarity of the image, with smaller entropy indicating better scattering point focusing.…”

Section: Model For Motion Error Compensationmentioning

confidence: 99%

“…To address this, IE has been used as a fitness function for ISAL phase error estimation in several works. 9,17,18 The concentration of image energy distribution is directly proportional to the clarity of the image, with smaller entropy indicating better scattering point focusing. Frieden 19 has defined various forms of IE with one of them being chosen for the current problem E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 2 5 ; 1 1 4 ; 2 6 6…”

Section: Model For Phase Error Estimationmentioning

confidence: 99%

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Adaptive motion error compensation method based on bat algorithm for maneuvering targets in inverse synthetic aperture LiDAR imaging

Li,

Jin,

et al. 2023

Opt. Eng.

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Motion error compensation is a crucial aspect of processing inverse synthetic aperture light detection and ranging data. Motion phase error occurs mainly due to the relative motion between the target and the system, as well as vibration of either the system or the target, which significantly affects the image quality of optical synthetic aperture radar. Since spatial targets usually have a non-cooperative motion state with a high degree of motion parameter uncertainty, accurate estimation of cross-range phase error becomes challenging due to the presence of envelope tilt effect. We propose an adaptive compensation method that handles motion errors of maneuvering targets by estimating and compensating various types of errors introduced by the target motion process. Using the geometric and signal models to analyze error components, a compensation model is established that uses envelope contrast and image entropy as fitness functions. The bat algorithm is employed to solve this error model. Simulation and outdoor experimental results demonstrate that the proposed algorithm offers higher accuracy and better stability compared to traditional optimization algorithms.

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Section: Model For Motion Error Compensationmentioning

confidence: 99%

Section: Model For Phase Error Estimationmentioning

confidence: 99%

Adaptive motion error compensation method based on bat algorithm for maneuvering targets in inverse synthetic aperture LiDAR imaging

Li,

Jin,

et al. 2023

Opt. Eng.

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“…Laser radar, an active measurement technology utilizing laser for target detection and range measurement, compares the phase, the frequency, and other attributes of transmitted and echo signals to provide target-range information [1,2]. Its advantages, including a high resolution, strong directivity, and narrow beam width, make it indispensable in environmental perception, meteorological research, and high-precision ranging [3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

High Resolution Ranging with Small Sample Number under Low SNR Utilizing RIP-OMCS Strategy and AHRC l1 Minimization for Laser Radar

Xue,

Xing,

Gao

et al. 2024

Remote Sensing

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This manuscript presents a novel scheme to achieve high-resolution laser-radar ranging with a small sample number under low signal-to-noise ratio (SNR) conditions. To reduce the sample number, the Restricted Isometry Property-based optimal multi-channel coprime-sampling (RIP-OMCS) strategy is established. In the RIP-OMCS strategy, the data collected across multiple channels with very low coprime-sampling rates can record accurate range information on each target. Further, the asynchronous problem caused by channel sampling-time errors is considered. The sampling-time errors are estimated using the cross-correlation function. After canceling the asynchronous problem, the data collected by multiple channels are then merged into non-uniform sampled signals. Using data combination, target-range estimation is converted into an optimization problem of sparse representation consisting of a non-uniform Fourier dictionary. This optimization problem is solved using adaptive hybrid re-weighted constraint (AHRC) l1 minimization. Two constraints are formed from statistical attributes of the targets and clutter. Moreover, as the detailed characteristics of the target, clutter, and noise are unknown before the solution, the two constraints can be adaptively modified, which guarantees that l1 minimization obtains the high-resolution range profile and accurate distance of all targets under a low SNR. Our experiments confirmed the effectiveness of the proposed method.

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DRN-VideoSR: a deep recursive network for video super-resolution based on a deformable convolution shared-assignment network

Zhang²,

Jiang³

2022

Multimed Tools Appl

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Noise-Robust Vibration Phase Compensation for Satellite ISAL Imaging by Frequency Descent Minimum Entropy Optimization

Cited by 12 publications

References 42 publications

Adaptive motion error compensation method based on bat algorithm for maneuvering targets in inverse synthetic aperture LiDAR imaging

Adaptive motion error compensation method based on bat algorithm for maneuvering targets in inverse synthetic aperture LiDAR imaging

High Resolution Ranging with Small Sample Number under Low SNR Utilizing RIP-OMCS Strategy and AHRC l1 Minimization for Laser Radar

DRN-VideoSR: a deep recursive network for video super-resolution based on a deformable convolution shared-assignment network

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