Dr. Christopher Kreucher Profile

Dr. Christopher Kreucher

Technical Fellow & Senior Scientist

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Publications (16)

Proceedings Article | 13 June 2023 Presentation + Paper

Novel view synthesis with compressed sensing as data augmentation for SAR ATR

Katherine Banas, Tyler Hill, Chris Kreucher, Brian Raeker, Kyle Simpson, Kirk Weeks

Proceedings Volume 12520, 125200H (2023) https://doi.org/10.1117/12.2664250

KEYWORDS: Deep learning, Synthetic aperture radar, Data modeling, Automatic target recognition, Compressed sensing

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Proceedings Article | 13 June 2023 Presentation + Paper

Improving SAR ATR using synthetic data via transfer learning

Brian Raeker, Tyler Hill, Chris Kreucher, Katherine Banas, Kevin Tactac, Kyle Simpson, Kirk Weeks

Proceedings Volume 12520, 125200L (2023) https://doi.org/10.1117/12.2663615

KEYWORDS: Education and training, Synthetic aperture radar, Automatic target recognition, Network architectures, Deep learning

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Proceedings Article | 17 May 2016 Paper

An estimation error bound for pixelated sensing

Chris Kreucher, Kristine Bell

Proceedings Volume 9842, 98421E (2016) https://doi.org/10.1117/12.2224059

KEYWORDS: Data modeling, Sensors, Error analysis, Data processing, Detection and tracking algorithms, Target detection, Electro optical modeling, Signal to noise ratio, Radar, Signal processing

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Proceedings Article | 14 May 2016 Paper

Modern approaches in deep learning for SAR ATR

Michael Wilmanski, Chris Kreucher, Jim Lauer

Proceedings Volume 9843, 98430N (2016) https://doi.org/10.1117/12.2220290

KEYWORDS: Convolutional neural networks, Automatic target recognition, Synthetic aperture radar, Neural networks, Signal processing, Machine learning, Image classification, Stochastic processes, Image processing, Neurons

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Proceedings Article | 20 June 2014 Paper

Optimal fusion of video and RF data for detection and tracking with object occlusion

Benjamin Shapo, Christopher Kreucher

Proceedings Volume 9091, 909106 (2014) https://doi.org/10.1117/12.2042781

KEYWORDS: Sensors, Video, Data fusion, Radar, Cameras, Particle filters, Video surveillance, Antennas, Doppler effect, Image sensors

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Proceedings Article | 3 May 2012 Paper

Detection and tracking of prominent scatterers in SAR data

Benjamin Shapo, Mark Stuff, Christopher Kreucher, Ron Majewski

Proceedings Volume 8394, 83940G (2012) https://doi.org/10.1117/12.919250

KEYWORDS: Synthetic aperture radar, Doppler effect, Motion estimation, Detection and tracking algorithms, Sensors, Radar, Reflectors, Target detection, Error analysis, Filtering (signal processing)

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Proceedings Article | 1 May 2007 Paper

Multiplatform information-based sensor management: an inverted UAV demonstration

Chris Kreucher, John Wegrzyn, Michel Beauvais, Ralph Conti

Proceedings Volume 6578, 65780Y (2007) https://doi.org/10.1117/12.719395

KEYWORDS: Sensors, Robots, Unmanned aerial vehicles, Cameras, Algorithm development, Detection and tracking algorithms, Target detection, Surveillance, Global Positioning System, Signal processing

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Proceedings Article | 2 May 2006 Paper

An information-based approach to decentralized multiplatform sensor management

Christopher Kreucher, Keith Kastella, John Wegrzyn, Brent Rickenbach

Proceedings Volume 6249, 62490H (2006) https://doi.org/10.1117/12.665604

KEYWORDS: Sensors, Target detection, Surveillance, Motion models, Unmanned aerial vehicles, Detection and tracking algorithms, Kinematics, Sensor networks, Signal to noise ratio, Sensor performance

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Proceedings Article | 2 May 2006 Paper

A tracking approach to localization and synchronization in mobile ad-hoc sensor networks

Patrick Bidigare, Chris Kreucher, Ralph Conti

Proceedings Volume 6249, 62490I (2006) https://doi.org/10.1117/12.665618

KEYWORDS: Clocks, Doppler effect, Acoustics, Particles, Sensors, Motion models, Statistical analysis, Time metrology, Kinematics, Filtering (signal processing)

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Self localization is a term used to describe the ability of a network to automatically determine the location of its nodes, given little or no external information. Self localization is an enabling technology for many future capabilities; specifically those that rely on a large number of sensors that self organize to form a coherent system. Most prior work in this area focuses on centralized computation with stationary nodes and synchronized clocks. We report on preliminary results for a setting that is more general in three ways. First, nodes in the network are moving. This implies the pair-wise distances between nodes are not fixed and therefore an iterative tracking procedure is needed to estimate the time varying node positions. Second, we do not assume synchronization between clocks on different nodes. In fact, we allow the clocks to have both an unknown offset and to be running at differing rates (i.e., a drift). Third, our method is decentralized, so there is no need for a single entity withfaccess to all measurements. In this setup, each node in the network is responsible for estimating its state. The method is based on repeated pair-wise communication between nodes. We focus on two types of observables in this paper. First, we use the time between when a message was sent from one node and when it was received by another node. In the case of synchronized clocks and stationary nodes, this observable provides information about the distance between the nodes. In the more general case with non-synchronized clocks, this observable is coupled to the clock offsets and drifts as well as the distance between nodes. Second, we use the Doppler stretch observed by the receiving node. In the case of synchronized clocks, this observable provides information about the line of sight velocity between the nodes. In the case of non-synchronized clocks, this observable is coupled to the clock drift as well as the line of sight velocity. We develop a sophisticated mathematical representation that allows all of these effects to be accounted for simultaneously. We approach the problem from a Bayesian viewpoint, where measurements are accumulated over time and used to form a probability density on the state, conditioned on the measurements. What results is a recursive filtering (or tracking) algorithm that optimally merges the measurements. We show by simulation and illustrative data collections that our method provides an efficient decentralized method for determining the location of a collection of moving nodes.

Proceedings Article | 16 September 2005 Paper

A study of factors affecting multiple target tracking with a pixelized sensor

Mark Morelande, Christopher Kreucher, Keith Kastella

Proceedings Volume 5913, 59130L (2005) https://doi.org/10.1117/12.617978

KEYWORDS: Particles, Detection and tracking algorithms, Signal to noise ratio, Sensors, Algorithm development, Monte Carlo methods, Surveillance, Particle filters, Target detection, Solids

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Proceedings Article | 26 May 2005 Paper

Multi-platform information-based sensor management

Chris Kreucher, Keith Kastella, Alfred Hero

Proceedings Volume 5820, (2005) https://doi.org/10.1117/12.608436

KEYWORDS: Sensors, Motion models, Target detection, Physics, Surveillance, Signal to noise ratio, Kinematics, Particle filters, Detection and tracking algorithms, Data modeling

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Proceedings Article | 5 January 2004 Paper

Tracking multiple targets using a particle filter representation of the joint multitarget probability density

Chris Kreucher, Keith Kastella, Alfred Hero

Proceedings Volume 5204, (2004) https://doi.org/10.1117/12.502696

KEYWORDS: Particles, Kinematics, Particle filters, Surveillance, Sensors, Nonlinear filtering, Filtering (signal processing), Detection and tracking algorithms, Space sensors, Motion measurement

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Proceedings Article | 5 January 2004 Paper

Information-based sensor management for multitarget tracking

Chris Kreucher, Keith Kastella, Alfred Hero

Proceedings Volume 5204, (2004) https://doi.org/10.1117/12.502699

KEYWORDS: Sensors, Particles, Detection and tracking algorithms, Particle filters, Signal to noise ratio, Motion models, Surveillance, Sensor performance, Kinematics, Time metrology

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Proceedings Article | 16 August 2001 Paper

Multiple-model nonlinear filtering for low-signal ground target applications

Chris Kreucher, Keith Kastella

Proceedings Volume 4380, (2001) https://doi.org/10.1117/12.436946

KEYWORDS: Motion models, Nonlinear filtering, Filtering (signal processing), Systems modeling, Sensors, Stochastic processes, Electronic filtering, Roads, Performance modeling, Error analysis

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Proceedings Article | 13 July 2000 Paper

Nonlinear filtering for ground target applications

Keith Kastella, Chris Kreucher, Michael Pagels

Proceedings Volume 4048, (2000) https://doi.org/10.1117/12.391983

KEYWORDS: Nonlinear filtering, Motion models, Sensors, Roads, Filtering (signal processing), Finite difference methods, Electronic filtering, Target detection, Signal to noise ratio, Interference (communication)

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Proceedings Article | 30 July 1998 Paper

Tracking lane and pavement edges using deformable templates

Karl Kluge, Chris Kreucher, Sridhar Lakshmanan

Proceedings Volume 3364, (1998) https://doi.org/10.1117/12.317467

KEYWORDS: Detection and tracking algorithms, Roads, Visualization, Edge detection, Radar, Cameras, Sensors, Image sensors, Evolutionary algorithms, Image segmentation

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Showing 5 of 16 publications

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