The Experts below are selected from a list of 61464 Experts worldwide ranked by ideXlab platform
Gordon Farquharson - One of the best experts on this subject based on the ideXlab platform.
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IGARSS - A robust Phase Calibration algorithm for a dual-channel along-track interferometric FMCW SAR
2014 IEEE Geoscience and Remote Sensing Symposium, 2014Co-Authors: Huazeng Deng, Gordon FarquharsonAbstract:A new robust Phase Calibration technique to estimate and correct the range-dependent Phase differences between two receivers in an FMCW ATI SAR has been developed. Based on linear regression, this Calibration scheme jointly estimates the Phases for both SAR receivers and reproduces the range-dependent Phase ripple seen in the ATI interfergrams. After applying the Calibration results to the post-processing of SAR data, the Phase ripple seen in the previous interferograms is significantly reduced (by about 10 dB). This work has relevance to implementing compact low-cost FMCW interferometric SAR systems for geophysical research measurements.
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contrast based Phase Calibration for remote sensing systems with digital beamforming antennas
IEEE Transactions on Geoscience and Remote Sensing, 2013Co-Authors: Gordon Farquharson, Paco Lopezdekker, Stephen J FrasierAbstract:A contrast-based Phase Calibration algorithm for digital beamforming remote sensing radars using three contrast metrics is presented. The algorithm corrects time-varying antenna array Phase errors that defocus digital beamforming remote sensing radar imagery. Amplitude errors are treated by equalizing the received powers in all elements. As such, the algorithm does not produce an absolute (or radiometric) Calibration vector for the array. The performance of the algorithm is studied using a combination of simulated and real radar data under various conditions and is compared with a clutter-based Calibration algorithm. An analytical proof showing that maximizing the expected value of the 4-norm metric is equivalent to Phase-calibrating the image, except for a linear Phase offset, is provided. We find that the clutter Calibration algorithm performs best for statistically homogeneous scenes but that the contrast-Calibration algorithms perform better with scenes with larger contrast ratios.
Liangcai Cao - One of the best experts on this subject based on the ideXlab platform.
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Self-referenced multiple-beam interferometric method for robust Phase Calibration of spatial light modulator
Optics Express, 2019Co-Authors: Yunhui Gao, Liangcai CaoAbstract:Phase-only liquid crystal spatial light modulator has wide ranging applications that require accurate Phase retardance. The Phase Calibration of the spatial light modulator is therefore of vital importance. Available self-referenced Calibration methods face the challenges of high time consumption, low efficiency, and low stability against the conditions. A self-referenced multiple-beam interferometric method is proposed to derive the global grayscale-Phase response. As is presented theoretically and experimentally, the proposed method reduces the measuring time and improves the Calibration efficiency by encoding multiple fringes in a single hologram. Results also show that the method is equally accurate when compared with traditional two-beam interferometric method, whereas providing a greater robustness against measuring errors since the standard deviation is only 56% of that of the traditional method.
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Progress in Phase Calibration for Liquid Crystal Spatial Light Modulators
Applied Sciences, 2019Co-Authors: Liangcai CaoAbstract:Phase-only Spatial Light Modulator (SLM) is one of the most widely used devices for Phase modulation. It has been successfully applied in the field with requirements of precision Phase modulation such as holographic display, optical tweezers, lithography, etc. However, due to the limitations in the manufacturing process, the grayscale-Phase response could be different for every single SLM device, even varying on sections of an SLM panel. A diverse array of Calibration methods have been proposed and could be sorted into two categories: the interferometric Phase Calibration methods and the diffractive Phase Calibration methods. The principles of Phase-only SLM are introduced. The main Phase Calibration methods are discussed and reviewed. The advantages of these methods are analyzed and compared. The potential methods for different applications are suggested.
Yunhui Gao - One of the best experts on this subject based on the ideXlab platform.
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Self-referenced multiple-beam interferometric method for robust Phase Calibration of spatial light modulator
Optics Express, 2019Co-Authors: Yunhui Gao, Liangcai CaoAbstract:Phase-only liquid crystal spatial light modulator has wide ranging applications that require accurate Phase retardance. The Phase Calibration of the spatial light modulator is therefore of vital importance. Available self-referenced Calibration methods face the challenges of high time consumption, low efficiency, and low stability against the conditions. A self-referenced multiple-beam interferometric method is proposed to derive the global grayscale-Phase response. As is presented theoretically and experimentally, the proposed method reduces the measuring time and improves the Calibration efficiency by encoding multiple fringes in a single hologram. Results also show that the method is equally accurate when compared with traditional two-beam interferometric method, whereas providing a greater robustness against measuring errors since the standard deviation is only 56% of that of the traditional method.
Huazeng Deng - One of the best experts on this subject based on the ideXlab platform.
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IGARSS - A robust Phase Calibration algorithm for a dual-channel along-track interferometric FMCW SAR
2014 IEEE Geoscience and Remote Sensing Symposium, 2014Co-Authors: Huazeng Deng, Gordon FarquharsonAbstract:A new robust Phase Calibration technique to estimate and correct the range-dependent Phase differences between two receivers in an FMCW ATI SAR has been developed. Based on linear regression, this Calibration scheme jointly estimates the Phases for both SAR receivers and reproduces the range-dependent Phase ripple seen in the ATI interfergrams. After applying the Calibration results to the post-processing of SAR data, the Phase ripple seen in the previous interferograms is significantly reduced (by about 10 dB). This work has relevance to implementing compact low-cost FMCW interferometric SAR systems for geophysical research measurements.
Yoram Bresler - One of the best experts on this subject based on the ideXlab platform.
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Blind Gain and Phase Calibration via Sparse Spectral Methods
arXiv: Information Theory, 2017Co-Authors: Kiryung Lee, Yoram BreslerAbstract:Blind gain and Phase Calibration (BGPC) is a bilinear inverse problem involving the determination of unknown gains and Phases of the sensing system, and the unknown signal, jointly. BGPC arises in numerous applications, e.g., blind albedo estimation in inverse rendering, synthetic aperture radar autofocus, and sensor array auto-Calibration. In some cases, sparse structure in the unknown signal alleviates the ill-posedness of BGPC. Recently there has been renewed interest in solutions to BGPC with careful analysis of error bounds. In this paper, we formulate BGPC as an eigenvalue/eigenvector problem, and propose to solve it via power iteration, or in the sparsity or joint sparsity case, via truncated power iteration. Under certain assumptions, the unknown gains, Phases, and the unknown signal can be recovered simultaneously. Numerical experiments show that power iteration algorithms work not only in the regime predicted by our main results, but also in regimes where theoretical analysis is limited. We also show that our power iteration algorithms for BGPC compare favorably with competing algorithms in adversarial conditions, e.g., with noisy measurement or with a bad initial estimate.
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Blind gain and Phase Calibration for low-dimensional or sparse signal sensing via power iteration
2017 International Conference on Sampling Theory and Applications (SampTA), 2017Co-Authors: Kiryung Lee, Yoram BreslerAbstract:Blind gain and Phase Calibration (BGPC) is a bilinear inverse problem involving the determination of unknown gains and Phases of the sensing system, and the unknown signal, jointly. BGPC arises in numerous applications, e.g., blind albedo estimation in inverse rendering, synthetic aperture radar autofocus, and sensor array auto-Calibration. In some cases, sparse structure in the unknown signal alleviates the ill-posedness of BGPC. Recently there has been renewed interest in solutions to BGPC with careful analysis of error bounds. In this paper, we formulate BGPC as an eigenvalue/eigenvector problem, and propose to solve it via power iteration, or in the sparsity or joint sparsity case, via truncated power iteration. Under certain assumptions, the unknown gains, Phases, and the unknown signal can be recovered simultaneously. Numerical experiments show that power iteration algorithms work not only in the regime predicted by our main results, but also in regimes where theoretical analysis is limited. We also show that our power iteration algorithms for BGPC compare favorably with competing algorithms in adversarial conditions, e.g., with noisy measurement or with a bad initial estimate.
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Identifiability in Bilinear Inverse Problems With Applications to Subspace or Sparsity-Constrained Blind Gain and Phase Calibration
IEEE Transactions on Information Theory, 2017Co-Authors: Kiryung Lee, Yoram BreslerAbstract:Bilinear inverse problems (BIPs), the resolution of two vectors given their image under a bilinear mapping, arise in many applications. Without further constraints, BIPs are usually ill-posed. In practice, the properties of natural signals are exploited to solve BIPs. For example, subspace constraints or sparsity constraints are imposed to reduce the search space. These approaches have shown some success in practice. However, there are few results on uniqueness in BIPs. For most BIPs, the fundamental question of under what condition the problem admits a unique solution is yet to be answered. For example, blind gain and Phase Calibration (BGPC) is a structured BIP, which arises in many applications, including inverse rendering in computational relighting (albedo estimation with unknown lighting), blind Phase and gain Calibration in sensor array processing, and multichannel blind deconvolution (MBD). It is interesting to study the uniqueness of such problems. In this paper, we define identifiability of a BIP up to a group of transformations. We derive necessary and sufficient conditions for such identifiability, i.e., the conditions under which the solutions can be uniquely determined up to the transformation group. These conditions take the form of dividing the identifiability of the pair of unknown variables into the individual identifiability of each variable. Although verifying these individual conditions requires problem-specific procedures, this framework is universally applicable to all BIPs. Applying these results to BGPC, we derive sufficient conditions for unique recovery under several scenarios, including subspace, joint sparsity, and sparsity models. For BGPC with joint sparsity or sparsity constraints, we develop a procedure to compute the transformation groups corresponding to inherent ambiguities. We also give necessary conditions in the form of tight lower bounds on sample complexities, and demonstrate the tightness of these bounds by numerical experiments. The results for BGPC not only demonstrate the application of the proposed general framework for identifiability analysis, but are also of interest in their own right.
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Optimal Sample Complexity for Blind Gain and Phase Calibration
IEEE Transactions on Signal Processing, 2016Co-Authors: Kiryung Lee, Yoram BreslerAbstract:Blind gain and Phase Calibration (BGPC) is a structured bilinear inverse problem, which arises in many applications, including inverse rendering in computational relighting (albedo estimation with unknown lighting), blind Phase and gain Calibration in sensor array processing, and multichannel blind deconvolution. The fundamental question of the uniqueness of the solutions to such problems has been addressed only recently. In a previous paper, we proposed studying the identifiability in bilinear inverse problems up to transformation groups. In particular, we studied several special cases of blind gain and Phase Calibration, including the cases of subspace and joint sparsity models on the signals, and gave sufficient and necessary conditions for identifiability up to certain transformation groups. However, there were gaps between the sample complexities in the sufficient conditions and the necessary conditions. In this paper, under a mild assumption that the signals and models are generic, we bridge the gaps by deriving tight sufficient conditions with optimal or near optimal sample complexities.