2016
DOI: 10.1016/j.optcom.2016.06.089
|View full text |Cite
|
Sign up to set email alerts
|

Atmospheric turbulence induced synthetic aperture lidar phase error compensation

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4

Citation Types

0
3
0

Year Published

2017
2017
2024
2024

Publication Types

Select...
5
1

Relationship

0
6

Authors

Journals

citations
Cited by 6 publications
(5 citation statements)
references
References 26 publications
0
3
0
Order By: Relevance
“…Over the past decade, most previous simulation and experiment works on ISAL and SAL imaging in atmospheric turbulence have been demonstrated [4][5][6][7][8][9][10][11]. In 2014, the performance characterization of phase gradient autofocus for ISAL was studied by CJ Pellizzari [12], who showed that the phase gradient algorithm (PGA) can still maintain its advantage of automatic focusing under atmospheric turbulence and low signal-to-noise ratio (SNR) conditions.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Over the past decade, most previous simulation and experiment works on ISAL and SAL imaging in atmospheric turbulence have been demonstrated [4][5][6][7][8][9][10][11]. In 2014, the performance characterization of phase gradient autofocus for ISAL was studied by CJ Pellizzari [12], who showed that the phase gradient algorithm (PGA) can still maintain its advantage of automatic focusing under atmospheric turbulence and low signal-to-noise ratio (SNR) conditions.…”
Section: Introductionmentioning
confidence: 99%
“…In 2014, the performance characterization of phase gradient autofocus for ISAL was studied by CJ Pellizzari [12], who showed that the phase gradient algorithm (PGA) can still maintain its advantage of automatic focusing under atmospheric turbulence and low signal-to-noise ratio (SNR) conditions. In 2016, based on a Monte Carlo random factor, Lu Tian-an [6] used a Kolmogorov phase screen to simulate turbulence and rank one phase error estimation to compensate SAL images. Russell Trahan [3] demonstrated the correction capability of the PGA algorithm under a low carrier-to-noise ratio (CNR) and atmospheric turbulence through experimentation.…”
Section: Introductionmentioning
confidence: 99%
“…Generation of high power ultrashort optical pulses, frequency conversion, optical coherence tomography, nonlinear microscopy and remote sensing applications are some of the examples that these type of laser sources potentially offer as significant benefits [1][2][3][4]. Furthermore, for 3-D mapping of (i) spectral indicators of Earth features and high precision ground topography, (ii) atmospheric species and physical attributes, these laser sources provide a powerful tool in air-borne and space-borne remote sensing in laser-based lidar and altimetry such as resonant backscatter lidar and ground vegetation biomass/bio-health detection applications [1,[5][6][7][8]. On the other hand, cutting-edge laser technologies with airborne and space-borne qualification are needed for these remote sensing applications where strict reqirements, especially for space-borne applications, severely narrows the class of lasers that can be utilized in the space environment.…”
Section: Introductionmentioning
confidence: 99%
“…Nonlinear microscopy, optical coherence tomography, frequency conversion, generation of high power ultrashort optical pulses and remote sensing applications can be given as some examples Koechner, 2003;). In air-borne (and also in space-borne) remote sensing with laser-based lidar and altimetry techniques, such as resonant backscatter lidar, ground vegetation bio-mass/biohealth detection etc., such laser sources could provide a powerful tool for 3-D mapping of atmospheric species and physical attributes, spectral indicators of Earth features and high precision ground topography Eitel, 2011;Lu, 2016;Pelon, 1986;Milton, 1997;Chen, 2014). This would provide a valuable source for understanding the atmospheric science and health of the Earth vegetation and ecological system (Eitel, 2011).…”
Section: Introductionmentioning
confidence: 99%