Jigsaw phase III: a miniaturized airborne 3-D imaging laser radar with photon-counting sensitivity for foliage penetration

Vaidyanathan, M.; Blask, Steven; Higgins, Thomas; Clifton, William; Davidsohn, Daniel; Carson, R.R.; Reynolds, Van; Pfannenstiel, Joanne; Cannata, Richard; Marino, Richard M.; Drover, John W.; Hatch, R. E.; Schue, David R.; Freehart, Robert E.; Rowe, Greg; Mooney, James; Hart, Carl R.; Stanley, Byron; McLaughlin, Joseph L.; Lee, Eui-In; Berenholtz, Jack; Aull, Brian F.; Zayhowski, J. J.; Vasile, Alex; Ramaswami, Prem; Ingersoll, K. A.; Amoruso, Thomas; Khan, Imran; Davis, W.; Heinrichs, Richard

doi:10.1117/12.722880

Cited by 21 publications

(17 citation statements)

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“…Nominal PMT dark noise rates are typically reported in PMT manufacturer specifications. The total mean number of noise photoelectrons per range bin is then (4) where is the PMT dark noise rate in counts per second and is the length of a range bin in seconds.…”

Section: Noise Modelmentioning

confidence: 99%

“…More recent systems demonstrating the low-SNR concept include MIT Lincoln Laboratory's Jigsaw sensor [4], the RULLI system developed by Los Alamos National Laboratory (LANL) [5], and prototype sensors developed by NASA [6], [7] and the University of Florida [8]. Until recently, the high cost of custom sensor components restricted the development of LSNR sensors to government and research facilities.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Small Target Detection Performance of Low-SNR Airborne Lidar

Cossio

Slatton

et al. 2010

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Recent technological advances in the performance of small micro-lasers and multi-channel multi-event photo-detectors have enabled the development of experimental airborne lidar (light detection and ranging) systems based on a low-SNR (LSNR) paradigm. Due to dense point spacing (tens of points per square meter) and sub-decimeter range resolution, LSNR lidar can likely enable detection of meter-scale targets that would go unnoticed by traditional lidar technology. Small vehicle obstructions and other similar targets in the beach and littoral zones are of particular interest, because of LSNR lidar's applicability to the near-shore environment and the general desire to improve detection of antivehicle and antipersonnel obstacles in the coastal zone.A target detection procedure is presented that exploits the detailed information available from LSNR lidar data while diminishing the effect of spurious noise events. Consideration is given to detection in both topographic and bathymetric scenarios. Data sets for target detection analysis are supplied by a numerical sensor simulator developed at the University of Florida. Target detection performance is evaluated as a function of environmental characteristics, such as water clarity and depth, and system parameters, specifically transmitted pulse energy and laser pulse repetition frequency. Analysis of results with regards to consideration for future system design is discussed.

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Section: Noise Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Small Target Detection Performance of Low-SNR Airborne Lidar

Cossio

Slatton

et al. 2010

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Because of the specific property of active laser detection, the accuracy of active laser detection is higher than traditional detective means. It has been widely used in many different fields, for instance, land and resources survey [4], forest fire warning [5,6] and maritime search and rescue [7,8] etc. The active laser detecting imaging at complex weather conditions is becoming an urgent issue [9].…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional imaging simulation of active laser detection based on DLOS method

Zhang

Zhou

Chen

et al. 2016

Infrared Physics & Technology

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“…The reflection pulse shape distortion, for one, also leads to TOF error [5][6][7] . Although limited by the laser footprint, this kind of error becomes more serious for the longer detection distance and better time measure devices [8,9] . According to the discussion of pulse distortion, the relation between time error and target slope is analyzed in this paper.…”

Section: Introductionmentioning

confidence: 99%

A compensation method for 3D imaging LADAR based on detected surface slope

Zhao

Ren

et al. 2013

SPIE Proceedings

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Basing on the measurement of pulse time-of-flight, 3D imaging LADAR have ability to obtain the profile of target surface. As the convolution results of impulse response function of footprint and probe pulse, the reflected pulse will distort if the footprint contain a variety of distances.After discriminate process, this distort brings in a time error for distance measurement, which becomes the anamorphose on system imaging further more. According to the discussion of time-dependent scattering cross section, this kind of anamorphose is mainly decided by the slope of target surface when the system parameters and target distance are determined. A compensation method for height error based on detected slope has been put forward. First of all, the slope distribution of detected surface could be calculated from the point cloud data by two-way difference method. Then, the approximate compensation height error will be obtained, according to the slope-error relationship by assuming that each footprint be a tilting plane. After adding to the detected data, the first approximate target surface data has been acquired. The compensation result will approaching the real value by repeating the three steps above. As an example, simulation analysis of Gaussian pulse imaging detection has been given.The result shows that this compensation method is effective and efficient.

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Jigsaw phase III: a miniaturized airborne 3-D imaging laser radar with photon-counting sensitivity for foliage penetration

Cited by 21 publications

References 0 publications

Predicting Small Target Detection Performance of Low-SNR Airborne Lidar

Predicting Small Target Detection Performance of Low-SNR Airborne Lidar

Three-dimensional imaging simulation of active laser detection based on DLOS method

A compensation method for 3D imaging LADAR based on detected surface slope

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