Polarization-discrimination technique to maximize the lidar signal-to-noise ratio for daylight operations

Hassebo, Yasser; Gross, Barry; Oo, M. M.; Moshary, Fred; Ahmed, Samir

doi:10.1364/ao.45.005521

Cited by 14 publications

(5 citation statements)

References 13 publications

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“…This can be approached using two polarizers at the transmitter and the receiver optics (Hassebo, B. Gross et al 2005;Hassebo, Barry M. Gross et al 2005;Hassebo, B. Gross et al 2006).…”

Section: The First Methods Is Running Raman Lidar Within the Solar-blimentioning

confidence: 99%

Active Remote Sensing: Lidar SNR Improvements

Hassebo¹

2012

Remote Sensing - Advanced Techniques and Platforms

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“…This can be approached using two polarizers at the transmitter and the receiver optics (Hassebo, B. Gross et al 2005;Hassebo, Barry M. Gross et al 2005;Hassebo, B. Gross et al 2006).…”

Section: The First Methods Is Running Raman Lidar Within the Solar-blimentioning

confidence: 99%

Active Remote Sensing: Lidar SNR Improvements

Hassebo¹

2012

Remote Sensing - Advanced Techniques and Platforms

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“…Sky brightness is the radiation caused by these factors. Sky brightness is an important direction of research on the detection and recognition of targets in space [37], the tracking and imaging of targets in space [38], and the inversion of the optical properties of aerosols and clouds [39]. Figure 5 shows the spectral curve of sky background light radiance.…”

Section: Parameter Simulation Of Hyperspectral Lidarmentioning

confidence: 99%

Parameter Simulation and Design of an Airborne Hyperspectral Imaging LiDAR System

Qian

Song

et al. 2021

Remote Sensing

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With continuous technological development, the future development trend of LiDAR in the field of remote sensing and mapping is to obtain the elevation and spectral information of ground targets simultaneously. Airborne hyperspectral imaging LiDAR inherits the advantages of active and passive remote sensing detection. This paper presents a simulation method to determine the design parameters of an airborne hyperspectral imaging LiDAR system. In accordance with the hyperspectral imaging LiDAR equation and optical design principles, the atmospheric transmission model and the reflectance spectrum of specific ground targets are utilized. The design parameters and laser emission spectrum of the hyperspectral LiDAR system are considered, and the signal-to-noise ratio of the system is obtained through simulation. Without considering the effect of detector gain and electronic amplification on the signal-to-noise ratio, three optical fibers are coupled into a detection channel, and the power spectral density emitted by the supercontinuum laser is simulated by assuming that the signal-to-noise ratio is equal to 1. The power spectral density emitted by the laser must not be less than 15 mW/nm in the shortwave direction. During the simulation process, the design parameters of the hyperspectral LiDAR system are preliminarily demonstrated, and the feasibility of the hyperspectral imaging LiDAR system design is theoretically guaranteed in combination with the design requirements of the supercontinuum laser. The spectral resolution of a single optical fiber of the hyperspectral LiDAR system is set to 2.5 nm. In the actual prototype system, multiple optical fibers can be coupled into a detection channel in accordance with application needs to further improve the signal-to-noise ratio of hyperspectral LiDAR system detection.

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“…Daytime observation capability was realized with dualwavelength, high-altitude lidar sodium fluorescence channel by using a sodium atomic filter [Cheng et al 2008, Hua et al 2005. A novel technique to reduce the sky background signal for a linearly polarized lidar was explored by using a polarization-selection technique [Hassebo et al 2006] where the sky background noise is minimized by taking advantage of the naturally occurring polarization properties in scattered skylight. This has shown good improvements in the signal-to-noise ratio (SNR) over conventional schemes.…”

Section: Subject Classificationmentioning

confidence: 99%

“…For most cases, SNR = 1 is the criterion for carrying out cloud and related studies. The height improvement of 33% at SNR = 1, as shown in Figure 8 for the etalon-based receiver, can be compared with the achieved value of 34% by the polarization discrimination technique [Hassebo et al 2006] at SNR = 10. Further, SNR at the cloud top is good, to build a slope for retrieving the atmospheric parameters.…”

Section: Height Coverage Improvementsmentioning

confidence: 99%

Fabry–Perot interferometer as a solar background noise suppressor: application to daytime lidar

Raghunath

Ramesh

Reddy

2012

Annals of Geophysics

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<p>Continuous atmospheric probing by a lidar is a requirement for many applications. However, due to high solar background noise during the daytime, lidar operations are mostly restricted to night-time. While many techniques are in practice, like reducing the receiver field of view, changing the view angle, introducing a narrow band Interference Filter (IF), these are applied to circumvent problems, rather than to suppress the noise. Using a Fabry-Perot interferometer as a narrow passband filter for solar background noise suppression is a known technique, and its potential is exploited in our system. An optical-fiber-coupled lidar system with its transmitter injection seeded was developed and has been operated during the daytime at Gadanki (13.6˚N, 79.2˚ E). The signal-to-noise ratio of the return signal is used as the performance indicator, to evaluate the improvements. Signal-to-noise ratios with and without the Fabry-Perot interferometer are measured with near identical test set-ups. The signal-to-noise ratio enhancement factor is ca. 4, in agreement with the theoretical value. The performance is compared when the receiver fields of view are changed.</p>

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Polarization-discrimination technique to maximize the lidar signal-to-noise ratio for daylight operations

Cited by 14 publications

References 13 publications

Active Remote Sensing: Lidar SNR Improvements

Active Remote Sensing: Lidar SNR Improvements

Parameter Simulation and Design of an Airborne Hyperspectral Imaging LiDAR System

Fabry–Perot interferometer as a solar background noise suppressor: application to daytime lidar

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