- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Cost analysis of 100% renewable electricity provider utilizing surplus electric power of residential PV systems in Japan
摘要： This paper introduces a framework for robust parameter estimation in multipass interferometric synthetic aperture radar (InSAR), such as persistent scatterer interferometry, SAR tomography, small baseline subset, and SqueeSAR. These techniques involve estimation of phase history parameters with or without covariance matrix estimation. Typically, their optimal estimators are derived on the assumption of stationary complex Gaussian-distributed observations. However, their statistical robustness has not been addressed with respect to observations with nonergodic and non-Gaussian multivariate distributions. The proposed robust InSAR optimization (RIO) framework answers two fundamental questions in multipass InSAR: 1) how to optimally treat images with a large phase error, e.g., due to unmolded motion phase, uncompensated atmospheric phase, etc.; and 2) how to estimate the covariance matrix of a non-Gaussian complex InSAR multivariate, particularly those with nonstationary phase signals. For the former question, RIO employs a robust M-estimator to effectively downweight these images; and for the latter, we propose a new method, i.e., the rank M -estimator, which is robust against non-Gaussian distribution. Furthermore, it can work without the assumption of sample stationarity, which is a topic that has not previously been addressed. We demonstrate the advantages of the proposed framework for data with large phase error and heavily tailed distribution, by comparing it with state-of-the-art estimators for persistent and distributed scatterers. Substantial improvement can be achieved in terms of the variance of estimates. The proposed framework can be easily extended to other multipass InSAR techniques, particularly to those where covariance matrix estimation is vital.
关键词： Differential interferometric synthetic aperture radar (D-InSAR),robust estimation,rank covariance matrix,robust InSAR optimization (RIO),M -estimator,SAR interferometry (InSAR)
更新于2025-09-23 15:19:57
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-band Geosynchronous D-InSAR System: Models and Analysis
摘要： The upcoming L-band geosynchronous differential interferometric synthetic aperture radar (GEO D-InSAR) system has the capability to monitor rapid deformations due to its excellent revisit capability. However, because of its low working frequency, the random ionospheric scintillation signal will degrade the deformation retrieval accuracy by giving rise to extra interferometric phase errors and obvious decorrelations in GEO D-InSAR interferograms. In this paper, aiming at impacts of ionospheric scintillations on GEO D-InSAR system, we theoretically establish its interferometric phase error and decorrelation models by using the scintillation statistical parameters directly. Simulations based on the scintillation sampling model, the Cornell university scintillation model, the phase screen mode, and the ionospheric scintillation signal acquired by the ground-based global positioning system receiver are carried out to verify the proposed model. Moreover, quantitative analyses of the ionospheric scintillation interferometric phase error and decorrelation impacts under different scintillation cases are obtained. The results verify that the proposed models and the analyses are effective. Meanwhile, they also suggest that the generated defocusing decorrelation dominates the ionospheric scintillation impacts on GEO D-InSAR, which can induce a coherence loss of more than 0.1 in the interferogram when only one image of the interferometric pair suffers the weak ionospheric scintillation.
关键词： Decorrelation effect,geosynchronous synthetic aperture radar (GEO SAR),differential interferometric synthetic aperture radar (D-InSAR),ionospheric scintillation
更新于2025-09-19 17:15:36
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[IEEE 2019 Compound Semiconductor Week (CSW) - Nara, Japan (2019.5.19-2019.5.23)] 2019 Compound Semiconductor Week (CSW) - GaSb/GaAs quantum nanostructures for intermediate band solar cell under high sunlight concentration
摘要： This paper introduces a framework for robust parameter estimation in multipass interferometric synthetic aperture radar (InSAR), such as persistent scatterer interferometry, SAR tomography, small baseline subset, and SqueeSAR. These techniques involve estimation of phase history parameters with or without covariance matrix estimation. Typically, their optimal estimators are derived on the assumption of stationary complex Gaussian-distributed observations. However, their statistical robustness has not been addressed with respect to observations with nonergodic and non-Gaussian multivariate distributions. The proposed robust InSAR optimization (RIO) framework answers two fundamental questions in multipass InSAR: 1) how to optimally treat images with a large phase error, e.g., due to unmolded motion phase, uncompensated atmospheric phase, etc.; and 2) how to estimate the covariance matrix of a non-Gaussian complex InSAR multivariate, particularly those with nonstationary phase signals. For the former question, RIO employs a robust M-estimator to effectively downweight these images; and for the latter, we propose a new method, i.e., the rank M -estimator, which is robust against non-Gaussian distribution. Furthermore, it can work without the assumption of sample stationarity, which is a topic that has not previously been addressed. We demonstrate the advantages of the proposed framework for data with large phase error and heavily tailed distribution, by comparing it with state-of-the-art estimators for persistent and distributed scatterers. Substantial improvement can be achieved in terms of the variance of estimates. The proposed framework can be easily extended to other multipass InSAR techniques, particularly to those where covariance matrix estimation is vital.
关键词： Differential interferometric synthetic aperture radar (D-InSAR),robust estimation,rank covariance matrix,robust InSAR optimization (RIO),M -estimator,SAR interferometry (InSAR)
更新于2025-09-19 17:13:59
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[IEEE 2019 International Conference on Electromechanical and Energy Systems (SIELMEN) - Craiova, Romania (2019.10.9-2019.10.11)] 2019 International Conference on Electromechanical and Energy Systems (SIELMEN) - Photovoltaic Technical Potential in Republic of Moldova
摘要： This paper introduces a framework for robust parameter estimation in multipass interferometric synthetic aperture radar (InSAR), such as persistent scatterer interferometry, SAR tomography, small baseline subset, and SqueeSAR. These techniques involve estimation of phase history parameters with or without covariance matrix estimation. Typically, their optimal estimators are derived on the assumption of stationary complex Gaussian-distributed observations. However, their statistical robustness has not been addressed with respect to observations with nonergodic and non-Gaussian multivariate distributions. The proposed robust InSAR optimization (RIO) framework answers two fundamental questions in multipass InSAR: 1) how to optimally treat images with a large phase error, e.g., due to unmolded motion phase, uncompensated atmospheric phase, etc.; and 2) how to estimate the covariance matrix of a non-Gaussian complex InSAR multivariate, particularly those with nonstationary phase signals. For the former question, RIO employs a robust M-estimator to effectively downweight these images; and for the latter, we propose a new method, i.e., the rank M -estimator, which is robust against non-Gaussian distribution. Furthermore, it can work without the assumption of sample stationarity, which is a topic that has not previously been addressed. We demonstrate the advantages of the proposed framework for data with large phase error and heavily tailed distribution, by comparing it with state-of-the-art estimators for persistent and distributed scatterers. Substantial improvement can be achieved in terms of the variance of estimates. The proposed framework can be easily extended to other multipass InSAR techniques, particularly to those where covariance matrix estimation is vital.
关键词： robust estimation,M -estimator,SAR interferometry (InSAR),robust InSAR optimization (RIO),rank covariance matrix,Differential interferometric synthetic aperture radar (D-InSAR)
更新于2025-09-19 17:13:59
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[IEEE IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society - Lisbon, Portugal (2019.10.14-2019.10.17)] IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society - Absolute Indoor Positioning-aided Laser-based Particle Filter Localization with a Refinement Stage
摘要： This paper introduces a framework for robust parameter estimation in multipass interferometric synthetic aperture radar (InSAR), such as persistent scatterer interferometry, SAR tomography, small baseline subset, and SqueeSAR. These techniques involve estimation of phase history parameters with or without covariance matrix estimation. Typically, their optimal estimators are derived on the assumption of stationary complex Gaussian-distributed observations. However, their statistical robustness has not been addressed with respect to observations with nonergodic and non-Gaussian multivariate distributions. The proposed robust InSAR optimization (RIO) framework answers two fundamental questions in multipass InSAR: 1) how to optimally treat images with a large phase error, e.g., due to unmolded motion phase, uncompensated atmospheric phase, etc.; and 2) how to estimate the covariance matrix of a non-Gaussian complex InSAR multivariate, particularly those with nonstationary phase signals. For the former question, RIO employs a robust M-estimator to effectively downweight these images; and for the latter, we propose a new method, i.e., the rank M -estimator, which is robust against non-Gaussian distribution. Furthermore, it can work without the assumption of sample stationarity, which is a topic that has not previously been addressed. We demonstrate the advantages of the proposed framework for data with large phase error and heavily tailed distribution, by comparing it with state-of-the-art estimators for persistent and distributed scatterers. Substantial improvement can be achieved in terms of the variance of estimates. The proposed framework can be easily extended to other multipass InSAR techniques, particularly to those where covariance matrix estimation is vital.
关键词： Differential interferometric synthetic aperture radar (D-InSAR),robust estimation,rank covariance matrix,robust InSAR optimization (RIO),M -estimator,SAR interferometry (InSAR)
更新于2025-09-16 10:30:52