A new phase-unwrapping method used in the single frequency estimation

2005 ◽  
Author(s):  
Xiaoqing Fang ◽  
Peixia Xu ◽  
Hui Li
Keyword(s):  
Frequency Estimation ◽  
Phase Unwrapping ◽  
Single Frequency ◽  
New Phase

scholarly journals Efficient and Accurate Frequency Estimator under Low SNR by Phase Unwrapping

2019 ◽  
Vol 2019 ◽  
pp. 1-6
Author(s):  
Shen Zhou ◽  
Liu Rongfang
Keyword(s):  
Frequency Estimation ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Likelihood Estimation ◽  
Accurate Method ◽  
Phase Unwrapping ◽  
Single Frequency ◽  
Low Snr ◽  
Cramer Rao Bound ◽  
Frequency Estimator

In the case of low signal-to-noise ratio, for the frequency estimation of single-frequency sinusoidal signals with additive white Gaussian noise, the phase unwrapping estimator usually performs poorly. In this paper, an efficient and accurate method is proposed to address this problem. Different from other methods, based on fast Fourier transform, the sampled signals are estimated with the variances approaching the Cramer-Rao bound, followed with the maximum likelihood estimation of the frequency. Experimental results reveal that our estimator has a better performance than other phase unwrapping estimators. Compared with the state-of-the-art method, our estimator has the same accuracy and lower computational complexity. Besides, our estimator does not have the estimation bias.

scholarly journals Photometric constraint for absolute phase unwrapping from single-frequency fringe patterns

2021 ◽  
Author(s):  
zhao shuai qi ◽  
Xiaojun Liu ◽  
Zhao Wang ◽  
jiaqi yang ◽  
Yanning Zhang
Keyword(s):  
Phase Unwrapping ◽  
Single Frequency ◽  
Absolute Phase ◽  
Fringe Patterns

3D InSAR Phase Unwrapping Within the Compressive Sensing Framework

2019 ◽  
pp. 809-841
Author(s):  
Wajih Ben Abdallah ◽  
Riadh Abdelfattah
Keyword(s):  
Compressive Sensing ◽  
General Problem ◽  
Estimation Error ◽  
Curvelet Transform ◽  
Problem Formulation ◽  
Phase Unwrapping ◽  
Interferometric Synthetic Aperture Radar ◽  
Phase Volume ◽  
The Relationship ◽  
New Phase

This chapter presents a new phase unwrapping algorithm for the 3D Interferometric Synthetic Aperture Radar (3D InSAR) volumes. The proposed approach is based on the relationship between the gradient vectors of the observed wrapped phase and the true phase respectively, when the Itoh condition is satisfied. Since this relationship is violated by the residue pixels in the observed wrapped phase, a general problem formulation which takes into account the estimation error due to these residue values is proposed. This approach exploits the temporal inter correlation between the interferometric frames within a compressive sensing framework. The 3D discrete curvelet transform is used in order to ensure a suitable sparse representation of the phase volume. The performance of the proposed 3D phase unwrapping algorithm is tested on simulated and real SAR 3D datasets

3D InSAR Phase Unwrapping within the Compressive Sensing Framework

2017 ◽  
pp. 125-157
Author(s):  
Wajih Ben Abdallah ◽  
Riadh Abdelfattah
Keyword(s):  
Compressive Sensing ◽  
General Problem ◽  
Estimation Error ◽  
Curvelet Transform ◽  
Problem Formulation ◽  
Phase Unwrapping ◽  
Interferometric Synthetic Aperture Radar ◽  
Phase Volume ◽  
The Relationship ◽  
New Phase

This chapter presents a new phase unwrapping algorithm for the 3D Interferometric Synthetic Aperture Radar (3D InSAR) volumes. The proposed approach is based on the relationship between the gradient vectors of the observed wrapped phase and the true phase respectively, when the Itoh condition is satisfied. Since this relationship is violated by the residue pixels in the observed wrapped phase, a general problem formulation which takes into account the estimation error due to these residue values is proposed. This approach exploits the temporal inter correlation between the interferometric frames within a compressive sensing framework. The 3D discrete curvelet transform is used in order to ensure a suitable sparse representation of the phase volume. The performance of the proposed 3D phase unwrapping algorithm is tested on simulated and real SAR 3D datasets

Least Squares Phase Unwrapping Algorithm Based on Topographic Factors

2011 ◽  
Vol 105-107 ◽  
pp. 1876-1879
Author(s):  
Wei Ke Liu ◽  
Gou Lin Liu ◽  
Xiao Qing Zhang
Keyword(s):  
Least Squares ◽  
Weighted Least Squares ◽  
Phase Unwrapping ◽  
Local Phase ◽  
Complex Signals ◽  
Topographic Factors ◽  
Interference Fringes ◽  
Phase Images ◽  
New Phase ◽  
Accuracy And Stability

The phase of complex signals is wrapped since it can only be measured modulo-2; unwrapping searches for the 2-combinations that minimize the discontinuity of the unwrapped phase, as only the unwrapped phase can be analyzed and interpreted by further processing. Weighted least squares phase unwrapping algorithm could avoid errors transmission in the whole phase images, but it could not avoid defect and overlay of interference fringes caused by topographic factors. Therefore, a new phase unwrapping and weights choosing method based on local phase frequency estimate of topographic factors was presented. Experiments show it is an efficient phase unwrapping method which well overcome the defect of under-estimate slopes by least squares algorithm, and has higher accuracy and stability than other methods.

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