&lt;title&gt;Three-dimensional laser radar with APD arrays&lt;/title&gt;

Heinrichs, Richard; Aull, Brian F.; Marino, Richard M.; Fouche, Daniel G.; McIntosh, Alexander K.; Zayhowski, J. J.; Stephens, Timothy; O’Brien, M.; Albotǎ, Marius A.

doi:10.1117/12.440098

Cited by 35 publications

(15 citation statements)

References 3 publications

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“…First the radiant flux density distribution on the target surface is calculated, and then the contribution to the irradiance at the receiver from individual surface element is calculated based on the principle of optical scattering. Subsequently, contribution from different points on a small surface area in the fields of view (FOV) [1] of single detector element is added for the irradiance on the corresponding pixel.…”

Section: Model Descriptionmentioning

confidence: 99%

“…Lincoln Laboratory has developed 3D imaging laser radar systems with passively Q-switched diode-pumped solid-state microchip lasers and arrays of avalanche photodiodes operating in the Geiger mode [1][2][3]. When entire scene is flood illuminated with a laser pulse, the backscattered light is imaged onto a two-dimensional array of detectors [4].…”

Section: Introductionmentioning

confidence: 99%

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GPU-accelerated Computation of 3D Laser Radar Range Imaging of Arbitrary Coarse Targets

Lin

et al. 2012

2012 IEEE 18th International Conference on Parallel and Distributed Systems

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We report here a backscattering model of average signal power function (SPF) for laser radar 3D range imagery obtained by arrays of detectors for arbitrary coarse targets. The model relates the average power seen by the receiver with laser pulse, target shape, optical scattering properties of surface material, incidence angle and other factors. The optical scattering property of the material is characterized by bidirectional reflectance distribution function (BRDF). The model can be used for demonstration of 3D laser radar system and can also be used to generate library of model data sets for automatic target recognition (ATR). Finally, Compute Unified Device Architecture (CUDA) is introduced for parallelizing these algorithms. The acceleration reaches 56 times speedup on single Fermi-generation NVIDIA GTX 480 as compared to the traditional CPU version of code on Intel i7 930 CPU.

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Section: Model Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GPU-accelerated Computation of 3D Laser Radar Range Imaging of Arbitrary Coarse Targets

Lin

et al. 2012

2012 IEEE 18th International Conference on Parallel and Distributed Systems

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“…A 24 GHz microwave radar sensor was used for this study -the active safety system. Heinrichs et al [16] and Ratches et al [17] studied automatic target recognition using radar range imagery.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic target recognition on land using three dimensional (3D) laser radar and artificial neural networks

Goztepe¹

2013

sajie

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During combat, measuring the dimensions of targets is extremely important for knowing when to fire on the enemy. The importance of identifying a known target on land emphasizes the importance of techniques devoted to automatic target recognition. Although a number of object-recognition techniques have been developed in the past, none of them have provided the desired specifics for unidentified target recognition. Studies on target recognition are largely based on images that assume that images of a known target can be readily viewed under any circumstance. But this is not true for military operations conducted on various terrains under specific circumstances. Usually it is not possible to capture images of unidentified objects because of weather, inadequate equipment, or concealment. In this study, a new approach that integrates neural networks and laser radar has been developed for automatic target recognition in order to reduce the abovementioned problems. Unlike current studies, the proposed model uses the geometric dimensions of unidentified targets in order to detect and recognise them under severe weather conditions. OPSOMMINGDie bepaling van teikenafmetings is van besondere belanggedurende gevegte sodat vuurtydstipte sodoende vasgelê kan word. In hierdie opsig word outomatiese uitkenning van teikentipe dus ook belangrik. Laasgenoemde tegnieke het desnieteenstaande nie besonder presteer met die uitkenning van vreemdeteikentipes nie. Terreintoestande, weersomstandighede, swak waarnemingstoerusting en kamoeflering speel in die verband ook 'n rol. Nuwerwetse toerusting wat gebruikmaak van neurale netwerke laserradar word voorgehou as 'n oplossing vir die vraagstuk onder uiteenlopende omgewingstoestande.

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“…13 Development work has continued to advance the technology for Geiger-mode ladar components, systems, data processing, and data exploitation in many research groups. Figure 5 shows a structure for a GMAPD detector.…”

Section: Calculations For Ingaas Geiger Mode Avalanchementioning

confidence: 99%

Comparison of flash lidar detector options

McManamon¹,

Banks

Beck³

et al. 2017

Opt. Eng

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Abstract. Three lidar receiver technologies using the total laser energy required to perform a set of imaging tasks are compared. The tasks are combinations of two collection types (3-D mapping from near and far), two scene types (foliated and unobscured), and three types of data products (geometry only, geometry plus 3-bit intensity, and geometry plus 6-bit intensity). The receiver technologies are based on Geiger mode avalanche photodiodes (GMAPD), linear mode avalanche photodiodes (LMAPD), and optical time-of-flight lidar, which combine rapid polarization rotation of the image and dual low-bandwidth cameras to generate a 3-D image. We choose scenarios to highlight the strengths and weaknesses of various lidars. We consider HgCdTe and InGaAs variations of LMAPD cameras. The InGaAs GMAPD and the HgCdTe LMAPD cameras required the least energy to 3-D map both scenarios for bare earth, with the GMAPD taking slightly less energy. We comment on the strengths and weaknesses of each receiver technology. Six bits of intensity gray levels requires substantial energy using all camera modalities.

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<title>Three-dimensional laser radar with APD arrays</title>

Cited by 35 publications

References 3 publications

GPU-accelerated Computation of 3D Laser Radar Range Imaging of Arbitrary Coarse Targets

GPU-accelerated Computation of 3D Laser Radar Range Imaging of Arbitrary Coarse Targets

Automatic target recognition on land using three dimensional (3D) laser radar and artificial neural networks

Comparison of flash lidar detector options

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