Dr. Neil Gordon Profile

Dr. Neil Gordon

at Defence Science and Technology Organisation

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Author

Publications (21)

Proceedings Article | 15 May 2012 Paper

H-PMHT for correlated targets

Samuel Davey, Monika Wieneke, Neil Gordon

Proceedings Volume 8393, 83930R (2012) https://doi.org/10.1117/12.919511

KEYWORDS: Image sensors, Sensors, Detection and tracking algorithms, Motion models, Expectation maximization algorithms, Image processing, Process modeling, Statistical analysis, Motion estimation, Image segmentation

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Proceedings Article | 16 September 2005 Paper

Tracking multiple targets with coordinated turn maneuvers

Mark Morelande, Neil Gordon

Proceedings Volume 5913, 59130F (2005) https://doi.org/10.1117/12.617966

KEYWORDS: Motion models, Particles, Particle filters, Detection and tracking algorithms, Monte Carlo methods, Target detection, Stochastic processes, Sensors, Electronic filtering, Data modeling

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Proceedings Article | 25 August 2004 Paper

Particle-based track-before-detect in Rayleigh noise

Mark Rutten, Neil Gordon, Simon Maskell

Proceedings Volume 5428, (2004) https://doi.org/10.1117/12.541275

KEYWORDS: Particles, Sensors, Target detection, Detection and tracking algorithms, Signal to noise ratio, Particle filters, Interference (communication), Signal processing, Signal detection, Radar

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Proceedings Article | 25 August 2004 Paper

Multipath track association for over-the-horizon radar using Lagrangian relaxation

Mark Rutten, Simon Maskell, Mark Briers, Neil Gordon

Proceedings Volume 5428, (2004) https://doi.org/10.1117/12.541276

KEYWORDS: Detection and tracking algorithms, Radar, Target detection, Computer simulations, Surveillance, Chromium, Lawrencium, Reconstruction algorithms, Defense and security, Signal processing

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Over-the-horizon radar (OTHR) uses the refraction of high frequency radiation through the ionosphere in order to detect targets beyond the line-of-sight horizon. The complexities of the ionosphere can produce multipath propagation, which may result in multiple resolved detections for a single target. When there are multipath detections, an OTHR tracker will produce several spatially separated tracks for each target. Information conveying the state of the ionosphere is required in order to determine the true location of the target and is available in the form of a set of possible propagation paths, and a transformation from measured coordinates into ground coordinates for each path. Since there is no a-priori information as to how many targets are in the surveillance region, or which propagation path gave rise to which track, there is a joint target and propagation path association ambiguity which must be resolved using the available track and ionospheric information. The multipath track association problem has traditionally been solved using a multiple hypothesis technique, but a shortcoming of this method is that the number of possible association hypotheses increases exponentially with both the number of tracks and the number of possible propagation paths. This paper proposes an algorithm based on a combinatorial optimisation method to solve the multipath track association problem. The association is formulated as a two-dimensional assignment problem with additional constraints. The problem is then solved using Lagrangian relaxation, which is a technique familiar in the tracking literature for the multidimensional assignment problem arising in data association. It is argued that due to a fundamental property of relaxations convergence cannot be guaranteed for this problem. However, results show that a multipath track-to-track association algorithm based on Lagrangian relaxation, when compared with an exact algorithm, provides a large reduction in computational effort, without significantly degrading association accuracy.

Proceedings Article | 25 August 2004 Paper

Tracking maneuvering targets using a scale mixture of normals

Simon Maskell, Neil Gordon, Nick Everett, Martin Robinson

Proceedings Volume 5428, (2004) https://doi.org/10.1117/12.542071

KEYWORDS: Particles, Motion models, Particle filters, Filtering (signal processing), Systems modeling, Error analysis, Electronic filtering, Performance modeling, Algorithm development, Modeling

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Proceedings Article | 5 January 2004 Paper

Application of particle filters to single-target tracking problems

Mark Morelande, Subhash Challa, Neil Gordon

Proceedings Volume 5204, (2004) https://doi.org/10.1117/12.503553

KEYWORDS: Particles, Particle filters, Electronic filtering, Detection and tracking algorithms, Target detection, Filtering (signal processing), Statistical analysis, Numerical simulations, Performance modeling, Error analysis

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Proceedings Article | 12 September 2003 Paper

Perspectives on dynamic infrared scene projection using positive and negative luminescence

Geoff Nash, Neil Gordon, Martin Emeny, Tim Ashley

Proceedings Volume 5092, (2003) https://doi.org/10.1117/12.512651

KEYWORDS: Solar concentrators, Infrared radiation, Photodiodes, Medium wave, Projection systems, Integrated optics, Sensors, Luminescence, Temperature metrology, Thermography

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Proceedings Article | 7 August 2002 Paper

Efficient particle filters for joint tracking and classification

Neil Gordon, Simon Maskell, Thiagalingam Kirubarajan

Proceedings Volume 4728, (2002) https://doi.org/10.1117/12.478524

KEYWORDS: Particle filters, Particles, Detection and tracking algorithms, Sensors, Radar, Filtering (signal processing), Kinematics, Monte Carlo methods, Systems modeling, FDA class I medical device development

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Proceedings Article | 7 August 2002 Paper

Efficient particle filtering for multiple target tracking with application to tracking in structured images

Simon Maskell, Malcolm Rollason, Neil Gordon, David Salmond

Proceedings Volume 4728, (2002) https://doi.org/10.1117/12.478509

KEYWORDS: Particles, Particle filters, Filtering (signal processing), Electronic filtering, Image filtering, Monte Carlo methods, Nonlinear filtering, Systems modeling, Silicon, Point spread functions

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Proceedings Article | 26 November 2001 Paper

Tracking airborne targets occasionally hidden in the blind Doppler

Neil Gordon, Branko Ristic

Proceedings Volume 4473, (2001) https://doi.org/10.1117/12.492809

KEYWORDS: Doppler effect, Particle filters, Radar, Particles, Monte Carlo methods, Electronic filtering, Filtering (signal processing), Motion models, Target detection, Clouds

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Proceedings Article | 13 July 2000 Paper

Group tracking with limited sensor resolution and finite field of view

David Salmond, Neil Gordon

Proceedings Volume 4048, (2000) https://doi.org/10.1117/12.392015

KEYWORDS: Sensors, Optical resolution, Particle filters, Particles, Neodymium, Space sensors, Target detection, Motion models, Electronic filtering, Composites

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Proceedings Article | 4 October 1999 Paper

Simulation-based optimal filter for maneuvering target tracking

Arnaud Doucet, Neil Gordon

Proceedings Volume 3809, (1999) https://doi.org/10.1117/12.364025

KEYWORDS: Monte Carlo methods, Particles, Filtering (signal processing), Detection and tracking algorithms, Signal processing, Expectation maximization algorithms, Particle filters, Electronic filtering, Optimal filtering, Computer simulations

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Proceedings Article | 4 October 1999 Paper

Sequential Monte Carlo for maneuvering target tracking in clutter

Neil Gordon, Arnaud Doucet

Proceedings Volume 3809, (1999) https://doi.org/10.1117/12.364045

KEYWORDS: Monte Carlo methods, Filtering (signal processing), Target detection, Expectation maximization algorithms, Detection and tracking algorithms, Particles, Signal processing, Electronic filtering, Computer simulations, Time metrology

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Proceedings Article | 4 October 1999 Paper

Group and extended object tracking

David Salmond, Neil Gordon

Proceedings Volume 3809, (1999) https://doi.org/10.1117/12.364028

KEYWORDS: Electro optical modeling, Sensors, Particle filters, Digital filtering, Affine motion model, Neodymium, Electronic filtering, Electro optical sensors, Nonlinear filtering, Motion models

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Proceedings Article | 25 June 1999 Paper

Detection and estimation of general frequency-modulated signals using reversible jump MCMC methods

Keith Copsey, Neil Gordon, Alan Marrs

Proceedings Volume 3816, (1999) https://doi.org/10.1117/12.351318

KEYWORDS: Modulation, Statistical analysis, Monte Carlo methods, Computer simulations, Fourier transforms, Signal to noise ratio, Statistical modeling, Signal detection, Image analysis, Environmental monitoring

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Proceedings Article | 3 September 1998 Paper

Tracking in the presence of intermittent spurious objects and clutter

David Salmond, David Fisher, Neil Gordon

Proceedings Volume 3373, (1998) https://doi.org/10.1117/12.324639

KEYWORDS: Sensors, Composites, Electronic filtering, Digital filtering, Optical resolution, Data modeling, Astatine, Target detection, Particle filters, Monte Carlo methods

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Proceedings Article | 3 September 1998 Paper

Bayesian sensor resource allocation

Neil Gordon, Mark Bedworth

Proceedings Volume 3373, (1998) https://doi.org/10.1117/12.324632

KEYWORDS: Stochastic processes, Monte Carlo methods, Control systems, Sensors, Target detection, Detection and tracking algorithms, Systems modeling, Statistical analysis, Algorithm development, Velocity measurements

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Proceedings Article | 30 July 1998 Paper

Target acquisition/selection: a comparison between a Bayesian technique and the Munkres algorithm

Neil Gordon, David Salmond, Nicholas Everett

Proceedings Volume 3365, (1998) https://doi.org/10.1117/12.317519

KEYWORDS: Target detection, Fluorescence correlation spectroscopy, Detection and tracking algorithms, Target acquisition, Monte Carlo methods, Missiles, Algorithm development, Distance measurement, Roads, Control systems

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Proceedings Article | 29 October 1997 Paper

Bayesian target tracking after group pattern distortion

Neil Gordon, David Salmond, David Fisher

Proceedings Volume 3163, (1997) https://doi.org/10.1117/12.283961

KEYWORDS: Neodymium, Target detection, Motion models, Sensors, Digital filtering, Distortion, Device simulation, Electronic filtering, Superposition, Clouds

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Proceedings Article | 31 May 1996 Paper

Bayesian target selection after group pattern distortion

Neil Gordon, David Salmond

Proceedings Volume 2759, (1996) https://doi.org/10.1117/12.241191

KEYWORDS: Target detection, Sensors, Neodymium, Motion models, Motion measurement, Detection and tracking algorithms, Time metrology, Distortion, Modeling, Information operations

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Proceedings Article | 1 September 1995 Paper

Bayesian approach to target tracking in the presence of glint

Neil Gordon, Angela Whitby

Proceedings Volume 2561, (1995) https://doi.org/10.1117/12.217720

KEYWORDS: Digital filtering, Monte Carlo methods, Filtering (signal processing), Radar, Nonlinear filtering, Detection and tracking algorithms, Systems modeling, Electronic filtering, Autoregressive models, Gaussian filters

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Showing 5 of 21 publications

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