Phase optimization algorithm for particle 3D localization with large axial depth

2021 ◽  
Author(s):  
Famin Wang ◽  
Hangfeng Li ◽  
Yun Xiao ◽  
Mengyuan Zhao ◽  
Yunhai Zhang
Keyword(s):  
Optimization Algorithm ◽  
3D Localization ◽  
Phase Optimization ◽  
Axial Depth

scholarly journals An Adaptive Phase Optimization Algorithm for Distributed Scatterer Phase History Retrieval

2021 ◽  
Vol 14 ◽  
pp. 3914-3926
Author(s):  
Shijin Li ◽  
Shubi Zhang ◽  
Tao Li ◽  
Yandong Gao ◽  
Qianfu Chen ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Phase Optimization

scholarly journals An Adaptive Weighted Phase Optimization Algorithm Based on the Sigmoid Model for Distributed Scatterers

2021 ◽  
Vol 13 (16) ◽  
pp. 3253
Author(s):  
Shijin Li ◽  
Shubi Zhang ◽  
Tao Li ◽  
Yandong Gao ◽  
Xiaoqing Zhou ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Bias Correction ◽  
Negative Influence ◽  
Eigenvalue Decomposition ◽  
Noise Robustness ◽  
Solution Strategy ◽  
Processing Efficiency ◽  
Low Coherence ◽  
Persistent Scatterers ◽  
Phase Optimization

Distributed scatterers (DSs) have been widely used in the time series interferometric synthetic aperture radar technique, which compensates for the insufficient density of persistent scatterers (PSs) in nonurban areas. In contrast to PS, DS is vulnerable to temporal and geometric decorrelation effects. Thus, phase optimization processing for DS is essential for reliable deformation parameter estimation. Advanced research has revealed that the application of all possible interferometric pairs will be more conducive to the reduction in phase biases. However, the low-coherence pixels will inevitably increase the difficulty of phase optimization and introduce unpredictable negative effects, which will reduce the effect of phase optimization. Therefore, this study proposed an advanced adaptive weighted phase optimization algorithm (AWPOA). In the AWPOA, the adaptive weighting strategy based on the sigmoid model was first proposed to assign more reasonable weights to pixels of different quality, which can efficiently reduce the negative influence of low-coherence pixels and improve the optimization performance. Moreover, coherence bias correction based on the second-kind statistics and an efficient solution strategy based on eigenvalue decomposition were derived and applied to achieve optimal phase series retrieval. The experimental results validated against both simulated and two sets of TerraSAR-X data demonstrated the overall superiority of the AWPOA over traditional phase optimization algorithms (POAs). Specifically, the processing efficiency of the eigenvalue decomposition solution strategy used in AWPOA was nearly 20 times faster than that of the PTA iterative solution strategy under the case without bias correction. Although bias correction increased the processing time, the optimization effect was significantly improved. Moreover, in terms of the quantitative evaluation indexes with the residual and the sum of the phase difference, the mean value of the improvement percentage of the AWPOA was increased by more than 12%, and the standard deviation was reduced by more than 1% over the traditional POAs, indicating its superior optimization performance and noise robustness.

A Two-Phase Optimization Algorithm For Mastermind

2007 ◽  
Vol 50 (4) ◽  
pp. 435-443 ◽  
Author(s):  
Shan-Tai Chen ◽  
Shun-Shii Lin ◽  
Li-Te Huang
Keyword(s):  
Optimization Algorithm ◽  
Two Phase ◽  
Phase Optimization
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