scholarly journals Iterated unscented Kalman filter for phase unwrapping of interferometric fringes

2016 ◽  
Vol 24 (17) ◽  
pp. 18872 ◽  
Author(s):  
Xianming Xie
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Phase Unwrapping

scholarly journals Adaptive Unscented Kalman Filter Phase Unwrapping Method and Its Application on Gaofen-3 Interferometric SAR Data

Sensors ◽  
2018 ◽  
Vol 18 (6) ◽  
pp. 1793 ◽  
Author(s):  
Yandong Gao ◽  
Shubi Zhang ◽  
Tao Li ◽  
Qianfu Chen ◽  
Shijin Li ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Phase Unwrapping ◽  
Adaptive Unscented Kalman Filter ◽  
Sar Data ◽  
Interferometric Sar

scholarly journals Practical phase unwrapping of interferometric fringes based on unscented Kalman filter technique

2015 ◽  
Vol 23 (25) ◽  
pp. 32337 ◽  
Author(s):  
Zhongtao Cheng ◽  
Dong Liu ◽  
Yongying Yang ◽  
Tong Ling ◽  
Xiaoyu Chen ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Phase Unwrapping ◽  
Filter Technique

scholarly journals Refined Two-Stage Programming Approach of Phase Unwrapping for Multi-Baseline SAR Interferograms Using the Unscented Kalman Filter

2019 ◽  
Vol 11 (2) ◽  
pp. 199 ◽  
Author(s):  
Yandong Gao ◽  
Shubi Zhang ◽  
Tao Li ◽  
Qianfu Chen ◽  
Xiang Zhang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Surface Deformation ◽  
Unscented Kalman Filter ◽  
Phase Unwrapping ◽  
Programming Approach ◽  
Interferometric Synthetic Aperture Radar ◽  
Two Stage ◽  
Second Stage ◽  
Digital Elevation ◽  
Single Baseline

Phase unwrapping (PU) represents a key step in the reconstruction of digital elevation models (DEMs) and the monitoring of surface deformation from interferometric synthetic aperture radar (InSAR) data. Compared with single-baseline (SB) PU, multi-baseline (MB) PU can resolve the phase discontinuities caused by noise and phase layover induced by terrain undulations. However, the MB PU performance is limited primarily by its poor robustness to measurement bias and noise. To address this problem, we propose a refined 2-D MB PU method based on the two-stage programming approach (TSPA). The proposed method uses the unscented Kalman filter (UKF) to improve the performance of the second stage of the original TSPA method. Specifically, the proposed method maintains the first stage of the TSPA to estimate the range and azimuth gradients between neighbouring pixels. Then, median filtering is slightly used to reduce the effects of the noise gradients on the estimated phase gradients. Finally, the UKF model is used to unwrap the interferometric phases using an efficient quality-guided strategy based on heap-sort. This paper is the first to integrate the UKF into the TSPA framework. The proposed method is validated using bistatic and monostatic MB InSAR datasets, and the experimental results show that the proposed method is effective for MB PU problems.

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