Dr. Thomas W. Altshuler Profile

Dr. Thomas W. Altshuler

VP and Group General Manager at Teledyne Marine Systems

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Publications (11)

Proceedings Article | 10 May 2018 Presentation

Advances in autonomous underwater vehicles and the move to network centric persistent subsea capabilities (Conference Presentation)

Thomas Altshuler, Clayton Jones, Robert Melvin, Daniel Shropshire, Joseph Borden

Proceedings Volume 10651, 106510K (2018) https://doi.org/10.1117/12.2306056

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Proceedings Article | 3 April 2008 Paper

Fault tolerant and lifetime control architecture for autonomous vehicles

Alexander Bogdanov, Yi-Liang Chen, Venkataraman Sundareswaran, Thomas Altshuler

Proceedings Volume 6981, 69810F (2008) https://doi.org/10.1117/12.779068

KEYWORDS: Control systems, Unmanned aerial vehicles, Actuators, Cameras, Sensors, Vehicle control, Instrument modeling, Servomechanisms, Computer architecture, Safety

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Proceedings Article | 2 May 2007 Paper

Multi-UAV autonomous collaborative behaviors for convoy protection

Y.-L. Chen, M. Peot, J. Lee, V. Sundareswaran, T. Altshuler

Proceedings Volume 6561, 65611C (2007) https://doi.org/10.1117/12.719993

KEYWORDS: Unmanned aerial vehicles, Sensors, Surveillance, Data modeling, Computer architecture, Relays, Unmanned systems, Human-machine interfaces, Network architectures, Sensor performance

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Proceedings Article | 2 May 2006 Paper

Autonomous collaborative behaviors for multi-UAV missions

Y.-L. Chen, M. Peot, J. Lee, V. Sundareswaran, T. Altshuler

Proceedings Volume 6249, 62490L (2006) https://doi.org/10.1117/12.673116

KEYWORDS: Unmanned aerial vehicles, Surveillance, Sensors, Fluorescence correlation spectroscopy, Target acquisition, Data modeling, Human-machine interfaces, Computer architecture, Defense and security, Unmanned systems

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Proceedings Article | 27 October 2005 Paper

An extensible architecture for collaborative networked unmanned air vehicles operations

Y.-L. Chen, M. Peot, J. Lee, T. Altshuler

Proceedings Volume 5986, 59860K (2005) https://doi.org/10.1117/12.634772

KEYWORDS: Unmanned aerial vehicles, Composites, Network architectures, Sensors, Fluorescence correlation spectroscopy, Relays, Control systems, Computer architecture, Computing systems, Target acquisition

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Proceedings Article | 27 October 2005 Paper

Planning sensing actions for UAVs in urban domains

Mark Peot, Thomas Altshuler, Arlen Breiholz, Richard Bueker, Kenneth Fertig, Aaron Hawkins, Sudhakar Reddy

Proceedings Volume 5986, 59860J (2005) https://doi.org/10.1117/12.634899

KEYWORDS: Unmanned aerial vehicles, Sensors, Modulation transfer functions, Target detection, Target recognition, Spatial frequencies, Systems modeling, Cameras, Visual process modeling, Detection and tracking algorithms

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Proceedings Article | 26 May 2005 Paper

Autonomous collaborative mission systems (ACMS) for multi-UAV missions

Y.-L. Chen, M. Peot, J. Lee, V. Sundareswaran, T. Altshuler

Proceedings Volume 5820, (2005) https://doi.org/10.1117/12.604617

KEYWORDS: Unmanned aerial vehicles, Sensors, Data modeling, Control systems, Composites, Fluorescence correlation spectroscopy, Computer architecture, Computing systems, Telecommunications, Human-machine interfaces

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Proceedings Article | 4 September 1998 Paper

Clutter and target signature statistics from the DARPA background clutter experiment

Erik Rosen, Thomas Altshuler

Proceedings Volume 3392, (1998) https://doi.org/10.1117/12.324153

KEYWORDS: Sensors, Target detection, Image segmentation, Chlorine, Algorithm development, Image processing, Copper, Land mines, Image sensors, Silicon

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Proceedings Article | 4 September 1998 Paper

DARPA background clutter data collection experiment: excavation results

Vivian George, Thomas Altshuler, Erik Rosen

Proceedings Volume 3392, (1998) https://doi.org/10.1117/12.324147

KEYWORDS: Sensors, Magnetometers, Land mines, Metals, Magnetic sensors, Magnetism, Electromagnetic coupling, Mining, Infrared sensors, General packet radio service

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Proceedings Article | 22 July 1997 Paper

Mine and UXO detection: measures of performance and their implication in real-world scenarios

Thomas Altshuler, Anne Andrews, David Sparrow

Proceedings Volume 3079, (1997) https://doi.org/10.1117/12.280859

KEYWORDS: Land mines, Target detection, Mining, Sensors, Unexploded object detection, Statistical analysis, Electromagnetism, Fourier transforms, Magnetism, Roads

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Mine and unexploded ordnance (UXO) detection systems must function in highly cluttered environments. Clutter leads to false alarms thereby hindering the detection and identification of targets of interest. Since the end user of the mine or UXO detection technology requires both a high detection rate and a low number of false alarms, technology demonstrations and system evaluations are designed to test these measures of performance. Detection rate and false- alarm rate are highly interdependent and must always be evaluated together. The relationship between the two rates directly affects the overall performance of the sensor in the field. Poor performance of a system in either detection rate or false-alarm rate causes a substantial increase in risk of undetected and thus unmarked or unremediated mines or UXO. A system with a low detection rate will leave many mines or UXO undetected. Performance can be traded between probability of detection and false-alarm rate by changing the system threshold. Raising or lowering the threshold will cause both the detections and false alarms to decrease or increase together. A system with a high false-alarm rate result in an increase in the time required to investigate potential targets. Therefore the rate of advance and rate of clearance decrease. With limited clearance resources, site coverage may become too time consuming or costly for operationally effective clearance, resulting in risk from undetected mines and UXO in areas that have not been searched. An assessment of the connection between detection rate nd false-alarm is presented. This relationship is discussed in the context of several government-sponsored in- field technology demonstrations of prototype and commercially available mine and UXO detection technologies, as well as real clearance operations. Implications of the results of these tests and the measures of performance are discussed in the context of real-world operations, including scenarios for clearance of miens in Bosnia and of UXO at DoD sites.

Proceedings Article | 22 July 1997 Paper

Quantifying performance of mine detectors with fewer than 10,000 targets

Anne Andrews, Vivian George, Thomas Altshuler

Proceedings Volume 3079, (1997) https://doi.org/10.1117/12.280858

KEYWORDS: Mining, Target detection, Land mines, Sensors, Roads, Sensor performance, Data analysis, Defense technologies, Defense and security, Error analysis

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The two primary measures of detection performance are probability of detection (P_D) and false alarm rate (FAR). Since these are statistical measures, the large statistical uncertainties associated with measurements on small data sets impose a severe limitation on the interpretation and extrapolation of the test results. The main difficulty with the data collected from many of the landmine detection experiments is that the number of mine targets is small, and the clutter data is measured on small areas. As a result of analyzing data from several detection tests for both UXO and mine detection technologies, the Institute for Defense Analyses (IDA) has generated a number of suggestions to improve the results obtained from future demonstrations and tests. In the absence of a rigorous error analysis, we can estimate the uncertainty in a probability of detection measurement with a simple model. If detections can be described as a binomial process weighted by the true probability of detection, then uncertainty equals (root)N, where N is the number of mines detected, and uncertainty in P_D (percent) equals (root)N/N X 100 for large N. A similar statistical uncertainty is encountered in the measurement of probability of false alarm. An opportunity for a false alarm can be defined by the amount of ground covered by the projected area of the target plus an allowable miss distance, say 1 m². Thus, for each 1 m² of ground that it passes over, the system has one opportunity to declare a false alarm. A test field that is 1 m by 20 m offers only 20 m² of area and thus only 20 samples for probability of false alarm measurement. A site that is 4 m wide and 4 km long covers 16,000 m². Thus, there will be a substantial increase in the clutter environment samples by the sensor and a concomitant improvement in false alarm measurements. Of course, false alarm density will be highly site dependent, so extrapolation of the result is still uncertain. Unfortunately, this limitation is insurmountable in single test and at a single site.

Showing 5 of 11 publications

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