Dr. Stephen Snarski Profile

Dr. Stephen Snarski

at Modern Technology Solutions Inc

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Author

Publications (9)

Proceedings Article | 6 June 2022 Presentation + Paper

Infrared search and track (IRST) for long-range, wide-area detect and avoid (DAA) on small unmanned aircraft systems (sUAS)

Steve Snarski, Richard Barnes, Noel Khan, Jonathan Baker, Jeff Webb

Proceedings Volume 12115, 1211504 (2022) https://doi.org/10.1117/12.2618065

KEYWORDS: Sensors, Infrared search and track, Infrared sensors, Target detection, Detection and tracking algorithms, Algorithm development, Image sensors, Data modeling, Unmanned aerial vehicles

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This paper describes results from an ongoing research and development effort to evaluate the potential for airborne long-range infrared target detection (aka, infrared search and track (IRST)) to meet critical requirements for small unmanned aircraft system (sUAS) airborne detect and avoid (DAA). Established sensors used for manned-aircraft airborne DAA are generally heavy, expensive, active, high-power devices that are difficult to scale to the size-weight-and-power/cost (SWaP/C) constraints of sUAS. Current low-SWaP sensors developed for sUAS DAA are not meeting the Well Clear (Safe Separation) detection range and coverage requirements to avoid non-cooperative aircraft. In this work, a low-SWaP staring IRST airborne DAA sensor payload system is being developed and tested to evaluate system performance against long range small airborne threats (e.g., sUAS, birds) and to guide system design studies. This paper presents results and analyses from a recent initial data collection using low-SWaP LWIR microbolometers suitable for Group 1-2 sUAS DAA applications against Group 1-2 sUAS targets. The modeling and results to date suggest that low-SWaP IR sensors such as those evaluated in this paper can provide the necessary detection range to support Group 1-2 sUAS DAA operations. Wide area coverage requirements, emerging from NASA and FAA UAS DAA studies, can be met through multiple cameras with decreasing angular resolution as they rotate aft to minimize overall system SWaP. Because of the limited SWaP capacity of sUAS, an IRST DAA system without extensive mechanical stabilization is desired. Analysis of UAS non-stabilized IR sensor vibration data found that the effects of motion blur on range performance were not significant and could be largely mitigated through deconvolution filters. The UAS sensor vibration introduced excessive scene clutter that interfered with effective UAS target tracking. The clutter was due to a time-scale conflict between the platform vibration energy and the baseline detector algorithms. Algorithms are being developed and tested to mitigate these effects and extend the baseline approach. Additional data reduction and analysis are planned to corroborate and extend these findings.

Proceedings Article | 16 May 2013 Paper

Testing and evaluation of a wearable augmented reality system for natural outdoor environments

David Roberts, Alberico Menozzi, James Cook, Todd Sherrill, Stephen Snarski, Pat Russler, Brian Clipp, Robert Karl, Eric Wenger, Matthew Bennett, Jennifer Mauger, William Church, Herman Towles, Stephen MacCabe, Jeffrey Webb, Jasper Lupo, Jan-Michael Frahm, Enrique Dunn, Christopher Leslie, Greg Welch

Proceedings Volume 8735, 87350A (2013) https://doi.org/10.1117/12.2015621

KEYWORDS: Sensors, Head, Cameras, Magnetism, Visualization, Augmented reality, Situational awareness sensors, Human-machine interfaces, Navigation systems, Magnetic sensors

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Proceedings Article | 22 May 2012 Paper

Soldier-worn augmented reality system for tactical icon visualization

David Roberts, Stephen Snarski, Todd Sherrill, Alberico Menozzi, Brian Clipp, Patrick Russler

Proceedings Volume 8383, 838305 (2012) https://doi.org/10.1117/12.921290

KEYWORDS: Magnetism, Commercial off the shelf technology, Augmented reality, Sensors, Cameras, Visualization, Navigation systems, Situational awareness sensors, Head, Magnetic sensors

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Proceedings Article | 8 May 2010 Paper

Robust extended target detection using nonlinear morphological operations

Hai-Wen Chen, Chris Volpe, Michael Tarnowski, Stephen Snarski

Proceedings Volume 7694, 76941H (2010) https://doi.org/10.1117/12.850511

KEYWORDS: Target detection, Image processing, Palladium, Sensors, Target recognition, Infrared sensors, Binary data, Nonlinear filtering, Infrared cameras, Image segmentation

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Proceedings Article | 5 May 2010 Paper

Graphical user interface concepts for tactical augmented reality

Chris Argenta, Anne Murphy, Jeremy Hinton, James Cook, Todd Sherrill, Steve Snarski

Proceedings Volume 7688, 76880I (2010) https://doi.org/10.1117/12.849462

KEYWORDS: Human-machine interfaces, Situational awareness sensors, Visualization, Augmented reality, Control systems, Prototyping, Sensors, Waveguides, Head, Eye

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Proceedings Article | 5 May 2010 Paper

Results of field testing with the FightSight infrared-based projectile tracking and weapon-fire characterization technology

Steve Snarski, Alberico Menozzi, Todd Sherrill, Chris Volpe, Mark Wille

Proceedings Volume 7666, 76662C (2010) https://doi.org/10.1117/12.850523

KEYWORDS: Cameras, Data acquisition, Prototyping, Detection and tracking algorithms, Weapons, Infrared cameras, Algorithm development, Acoustics, Sensors, Image processing

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Proceedings Article | 14 May 2007 Paper

Rapid high-confidence intelligent interior mapping

David Roberts, Dirk Warnaar, Alberico Menozzi, Soheil Saadat, Steve Snarski

Proceedings Volume 6561, 656102 (2007) https://doi.org/10.1117/12.719693

KEYWORDS: Image segmentation, Sensors, Environmental sensing, Lab on a chip, Intelligent sensors, Associative arrays, Optical sensors, Intelligence systems, Unmanned ground vehicles, Systems modeling

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Proceedings Article | 5 May 2006 Paper

Autonomous UAV-based mapping of large-scale urban firefights

Stephen Snarski, Karl Scheibner, Scott Shaw, Randy Roberts, Andy LaRow, Eric Breitfeller, Jasper Lupo, Darron Nielson, Bill Judge, Jim Forren

Proceedings Volume 6209, 620905 (2006) https://doi.org/10.1117/12.665310

KEYWORDS: Sensors, Acoustics, Infrared sensors, Unmanned aerial vehicles, Weapons, Infrared imaging, Data acquisition, Rockets, Environmental sensing, Infrared search and track

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This paper describes experimental results from a live-fire data collect designed to demonstrate the ability of IR and acoustic sensing systems to detect and map high-volume gunfire events from tactical UAVs. The data collect supports an exploratory study of the FightSight concept in which an autonomous UAV-based sensor exploitation and decision support capability is being proposed to provide dynamic situational awareness for large-scale battalion-level firefights in cluttered urban environments. FightSight integrates IR imagery, acoustic data, and 3D scene context data with prior time information in a multi-level, multi-step probabilistic-based fusion process to reliably locate and map the array of urban firing events and firepower movements and trends associated with the evolving urban battlefield situation. Described here are sensor results from live-fire experiments involving simultaneous firing of multiple sub/super-sonic weapons (2-AK47, 2-M16, 1 Beretta, 1 Mortar, 1 rocket) with high optical and acoustic clutter at ranges up to 400m. Sensor-shooter-target configurations and clutter were designed to simulate UAV sensing conditions for a high-intensity firefight in an urban environment. Sensor systems evaluated were an IR bullet tracking system by Lawrence Livermore National Laboratory (LLNL) and an acoustic gunshot detection system by Planning Systems, Inc. (PSI). The results demonstrate convincingly the ability for the LLNL and PSI sensor systems to accurately detect, separate, and localize multiple shooters and the associated shot directions during a high-intensity firefight (77 rounds in 5 sec) in a high acoustic and optical clutter environment with very low false alarms. Preliminary fusion processing was also examined that demonstrated an ability to distinguish co-located shooters (shooter density), range to <0.5 m accuracy at 400m, and weapon type. The combined results of the high-intensity firefight data collect and a detailed systems study demonstrate the readiness of the FightSight concept for full system development and integration.

Proceedings Article | 1 September 2004 Paper

Device for electro-magnetohydrodynamic (EMHD) energy harvesting

Stephen Snarski, Rolf Kasper, Anthony Bruno

Proceedings Volume 5417, (2004) https://doi.org/10.1117/12.545170

KEYWORDS: Electrodes, Magnetism, Energy harvesting, Electromagnetism, Sensors, Signal attenuation, Intelligence systems, Transducers, Intelligent sensors, Reconnaissance

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Showing 5 of 9 publications

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