Robust phase unwrapping algorithm for interferometric applications based on Zernike polynomial fitting and Wrapped Kalman Filter

2022 ◽  
Vol 152 ◽  
pp. 106952
Author(s):  
Zixin Zhao ◽  
Junxiang Li ◽  
Chen Fan ◽  
Yijun Du ◽  
Menghang Zhou ◽  
...  
2014 ◽  
Vol 26 (1) ◽  
pp. 017001 ◽  
Author(s):  
Zixin Zhao ◽  
Hong Zhao ◽  
Lu Zhang ◽  
Fen Gao ◽  
Yuwei Qin ◽  
...  

Author(s):  
Zixin Zhao ◽  
Menghang Zhou ◽  
Yijun Du ◽  
Junxiang Li ◽  
Chen Fan ◽  
...  

Abstract Phase unwrapping plays an important role in optical phase measurements. In particular, phase unwrapping under heavy noise conditions remains an open issue. In this paper, a deep learning-based method is proposed to conduct the phase unwrapping task by combining Zernike polynomial fitting and a Swin-Transformer network. In this proposed method, phase unwrapping is regarded as a regression problem, and the Swin-Transformer network is used to map the relationship between the wrapped phase data and the Zernike polynomial coefficients. Because of the self-attention mechanism of the transformer network, the fitting coefficients can be estimated accurately even under extremely harsh noise conditions. Simulation and experimental results are presented to demonstrate the outperformance of the proposed method over the other two polynomial fitting-based methods. This is a promising phase unwrapping method in optical metrology, especially in electronic speckle pattern interferometry.


2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Yan Wang ◽  
Yang Yan ◽  
Zhengjian Li ◽  
Long Cheng

The main factor affecting the localization accuracy is nonline of sight (NLOS) error which is caused by the complicated indoor environment such as obstacles and walls. To obviously alleviate NLOS effects, a polynomial fitting-based adjusted Kalman filter (PF-AKF) method in a wireless sensor network (WSN) framework is proposed in this paper. The method employs polynomial fitting to accomplish both NLOS identification and distance prediction. Rather than employing standard deviation of all historical data as NLOS detection threshold, the proposed method identifies NLOS via deviation between fitted curve and measurements. Then, it processes the measurements with adjusted Kalman filter (AKF), conducting weighting filter in the case of NLOS condition. Simulations compare the proposed method with Kalman filter (KF), adjusted Kalman filter (AKF), and Kalman-based interacting multiple model (K-IMM) algorithms, and the results demonstrate the superior performance of the proposed method. Moreover, experimental results obtained from a real indoor environment validate the simulation results.


2013 ◽  
Vol 52 (8) ◽  
pp. 085101 ◽  
Author(s):  
Fengtao Yan ◽  
Bin Fan ◽  
Xi Hou ◽  
Fan Wu

Sensors ◽  
2018 ◽  
Vol 18 (6) ◽  
pp. 1793 ◽  
Author(s):  
Yandong Gao ◽  
Shubi Zhang ◽  
Tao Li ◽  
Qianfu Chen ◽  
Shijin Li ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document