Prof. Simon Maskell Profile

Prof. Simon Maskell

at Univ of Liverpool

SPIE Involvement:

Author

Publications (15)

Proceedings Article | 1 November 2024 Presentation + Paper

Target tracking in a complex simulated world

Elias Griffith, Alexander Phillips, Liam Mai, Simon Maskell, Jason Ralph

Proceedings Volume 13200, 132000K (2024) https://doi.org/10.1117/12.3031696

KEYWORDS: Roads, Cameras, Detection and tracking algorithms, Bridges, Sensors, RGB color model, Data modeling, 3D modeling, Visualization, Environmental sensing

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Proceedings Article | 8 June 2022 Presentation + Paper

Stone Soup open source framework for tracking and state estimation: enhancements and applications

Jordi Barr, Oliver Harrald, Steven Hiscocks, Nicola Perree, Henry Pritchett, Sebastien Vidal, James Wright, Peter Carniglia, Emily Hunter, David Kirkland, Divy Raval, Siyuan Zheng, Anne Young, Bhashyam Balaji, Simon Maskell, Marcel Hernandez, Lyudmil Vladimirov

Proceedings Volume 12122, 1212205 (2022) https://doi.org/10.1117/12.2618495

KEYWORDS: Sensors, Detection and tracking algorithms, Radar, Filtering (signal processing), Data modeling, Gaussian filters, Algorithm development, Electronic filtering, Linear filtering, Video

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Proceedings Article | 4 October 2020 Presentation + Paper

Augmented inertial navigation using cold atom sensing

Alexander Phillips, Michael Wright, Marton Kiss-Toth, Iain Read, Isabelle Riou, Stephen Maddox, Simon Maskell, Jason Ralph

Proceedings Volume 11578, 115780C (2020) https://doi.org/10.1117/12.2583999

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Proceedings Article | 30 October 2012 Paper

Robust background subtraction for automated detection and tracking of targets in wide area motion imagery

Phil Kent, Simon Maskell, Oliver Payne, Sean Richardson, Larry Scarff

Proceedings Volume 8546, 85460Q (2012) https://doi.org/10.1117/12.965300

KEYWORDS: Sensors, Target detection, Surveillance, Image sensors, Image resolution, Detection and tracking algorithms, Image processing, Unmanned aerial vehicles, Statistical analysis, Image quality

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Proceedings Article | 19 May 2006 Paper

Fixed-lag sequential Monte Carlo data association

Mark Briers, Arnaud Doucet, Simon Maskell, Paul Horridge

Proceedings Volume 6236, 62360S (2006) https://doi.org/10.1117/12.665684

KEYWORDS: Monte Carlo methods, Detection and tracking algorithms, Particles, Manganese, Target detection, Stochastic processes, Particle filters, Computer simulations, Sensors, Algorithm development

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Proceedings Article | 15 September 2005 Paper

Poisson models for extended target and group tracking

Kevin Gilholm, Simon Godsill, Simon Maskell, David Salmond

Proceedings Volume 5913, 59130R (2005) https://doi.org/10.1117/12.618730

KEYWORDS: Particle filters, Particles, Sensors, Digital filtering, Electronic filtering, Statistical analysis, Motion models, Stochastic processes, Modeling, Superposition

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Proceedings Article | 25 August 2004 Paper

Fast mutual exclusion

Simon Maskell, Mark Briers, Robert Wright

Proceedings Volume 5428, (2004) https://doi.org/10.1117/12.542068

KEYWORDS: Detection and tracking algorithms, Filtering (signal processing), Electronic filtering, Particles, Particle filters

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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

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

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

Two-dimensional assignment with merged measurements using Lagrangian relaxation

Mark Briers, Simon Maskell, Mark Philpott

Proceedings Volume 5204, (2004) https://doi.org/10.1117/12.503834

KEYWORDS: Target detection, Particles, Detection and tracking algorithms, Particle filters, Binary data, Roads, Signal processing, Data processing, Optical resolution, Time metrology

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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 | 7 August 2002 Paper

Expected likelihood for tracking in clutter with particle filters

Alan Marrs, Simon Maskell, Yaakov Bar-Shalom

Proceedings Volume 4728, (2002) https://doi.org/10.1117/12.478507

KEYWORDS: Particle filters, Particles, Personal digital assistants, Sensors, Filtering (signal processing), Error analysis, Motion models, Analog electronics, Clouds, Statistical modeling

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Proceedings Article | 7 August 2002 Paper

Comparison of EKF, pseudomeasurement, and particle filters for a bearing-only target tracking problem

Xiangdong Lin, Thiagalingam Kirubarajan, Yaakov Bar-Shalom, Simon Maskell

Proceedings Volume 4728, (2002) https://doi.org/10.1117/12.478508

KEYWORDS: Particle filters, Nonlinear filtering, Monte Carlo methods, Particles, Filtering (signal processing), Electronic filtering, Sensors, Error analysis, Surveillance, Linear filtering

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Showing 5 of 15 publications

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