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2024

Target tracking in a complex simulated world

Griffith, E. J., Phillips, A. M., Mai, L. P., Maskell, S., & Ralph, J. F. (2024). Target tracking in a complex simulated world. In D. L. Hickman, H. Bürsing, P. J. Soan, & O. Steinvall (Eds.), Electro-Optical and Infrared Systems: Technology and Applications XXI (pp. 20). SPIE. doi:10.1117/12.3031696

DOI
10.1117/12.3031696
Conference Paper

Enhanced SMC<sup>2</sup>: Leveraging Gradient Information from Differentiable Particle Filters Within Langevin Proposals

Rosato, C., Murphy, J., Varsi, A., Horridge, P., & Maskell, S. (2024). Enhanced SMC<sup>2</sup>: Leveraging Gradient Information from Differentiable Particle Filters Within Langevin Proposals. In 2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) (pp. 1-8). IEEE. doi:10.1109/mfi62651.2024.10705779

DOI
10.1109/mfi62651.2024.10705779
Conference Paper

A qualitative exploration of barriers to efficient and effective structured medication reviews in primary care: Findings from the DynAIRx study.

Abuzour, A. S., Wilson, S. A., Woodall, A. A., Mair, F. S., Clegg, A., Shantsila, E., . . . Walker, L. E. (2024). A qualitative exploration of barriers to efficient and effective structured medication reviews in primary care: Findings from the DynAIRx study.. PloS one, 19(8), e0299770. doi:10.1371/journal.pone.0299770

DOI
10.1371/journal.pone.0299770
Journal article

Enhanced SMC$^2$: Leveraging Gradient Information from Differentiable Particle Filters Within Langevin Proposals

DOI
10.48550/arxiv.2407.17296
Preprint

A Poisson Multi-Bernoulli Mixture approach to tracking trains using Distributed Acoustic Sensing

Fontana, M., Hayder, T., Freilinger, W., García-Fernández, Á. F., & Maskell, S. (2024). A Poisson Multi-Bernoulli Mixture approach to tracking trains using Distributed Acoustic Sensing. In 2024 27th International Conference on Information Fusion (FUSION) (pp. 1-8). IEEE. doi:10.23919/fusion59988.2024.10706405

DOI
10.23919/fusion59988.2024.10706405
Conference Paper

COVID-19 risk-mitigation in reopening mass cultural events: population-based observational study for the UK Events Research Programme in Liverpool City Region

Burnside, G., Cheyne, C. P., Leeming, G., Humann, M., Darby, A., Green, M. A., . . . Buchan, I. E. (2024). COVID-19 risk-mitigation in reopening mass cultural events: population-based observational study for the UK Events Research Programme in Liverpool City Region. Journal of the Royal Society of Medicine. doi:10.1177/01410768231182389

DOI
10.1177/01410768231182389
Journal article

Response to the comment on: Exploring the impact of design criteria for reference sets on performance evaluation of signal detection algorithms: The case of drug-drug interactions.

Kontsioti, E., Maskell, S., & Pirmohamed, M. (2024). Response to the comment on: Exploring the impact of design criteria for reference sets on performance evaluation of signal detection algorithms: The case of drug-drug interactions.. Pharmacoepidemiology and drug safety, 33(1), e5731. doi:10.1002/pds.5731

DOI
10.1002/pds.5731
Journal article

2023

An O(log<sub>2</sub> N) SMC<sup>2</sup> Algorithm on Distributed Memory with an Approx. Optimal L-Kernel

Rosato, C., Varsi, A., Murphy, J., & Maskell, S. (2023). An O(log<sub>2</sub> N) SMC<sup>2</sup> Algorithm on Distributed Memory with an Approx. Optimal L-Kernel. In 2023 IEEE Symposium Sensor Data Fusion and International Conference on Multisensor Fusion and Integration (SDF-MFI) (pp. 1-8). IEEE. doi:10.1109/sdf-mfi59545.2023.10361452

DOI
10.1109/sdf-mfi59545.2023.10361452
Conference Paper

How might dynamic artificial intelligence (DynAIRx) be used to support prescribing to ensure efficient medication reviews?

Abuzour, A., Wilson, S., Woodall, A., Mair, F., Bollegala, D., Cant, H., . . . Walker, L. (2023). How might dynamic artificial intelligence (DynAIRx) be used to support prescribing to ensure efficient medication reviews?. American Academy of Family Physicians. doi:10.1370/afm.22.s1.4823

DOI
10.1370/afm.22.s1.4823
Report

A Shared Memory SMC Sampler for Decision Trees

Drousiotis, E., Varsi, A., Spirakis, P. G., & Maskell, S. (2023). A Shared Memory SMC Sampler for Decision Trees. In 2023 IEEE 35th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) (pp. 209-218). IEEE. doi:10.1109/sbac-pad59825.2023.00030

DOI
10.1109/sbac-pad59825.2023.00030
Conference Paper

Protecting Children from Online Exploitation: Can a Trained Model Detect Harmful Communication Strategies?

Cook, D., Zilka, M., DeSandre, H., Giles, S., & Maskell, S. (2023). Protecting Children from Online Exploitation: Can a Trained Model Detect Harmful Communication Strategies?. In Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (pp. 5-14). ACM. doi:10.1145/3600211.3604696

DOI
10.1145/3600211.3604696
Conference Paper

The Design of Relativistic Ultrafast Electron Diffraction and Imaging (RUEDI) Facility for Materials in Extremes.

Murooka, Y., Bryan, W., Clarke, J., Ellis, M., Kirkland, A. I., Maskell, S., . . . Browning, N. D. (2023). The Design of Relativistic Ultrafast Electron Diffraction and Imaging (RUEDI) Facility for Materials in Extremes.. Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada, 29(Supplement_1), 1487-1488. doi:10.1093/micmic/ozad067.764

DOI
10.1093/micmic/ozad067.764
Journal article

Repeated Filtering for Smoothing Particle Filters

Anderson, S. L., Stone, L. D., & Maskell, S. (2023). Repeated Filtering for Smoothing Particle Filters. Journal of Advances in Information Fusion, 18(1), 35-46.

Journal article

Machine learning assisted calibration of stochastic agent-based models for pandemic outbreak analysis

DOI
10.21203/rs.3.rs-2773605/v1
Preprint

Information fusion and tracking using Bernoulli filters for maritime surveillance

Ransom, M. J., Ralph, J. F., & Maskell, S. (2023). Information fusion and tracking using Bernoulli filters for maritime surveillance. In IET Conference Proceedings Vol. 2022 (pp. 477-482). Institution of Engineering and Technology (IET). doi:10.1049/icp.2022.2364

DOI
10.1049/icp.2022.2364
Conference Paper

Joint optimization of sonar waveform selection and sonobuoy placement

Taylor, C. M., Maskell, S., Narykov, A., & Ralph, J. F. (2023). Joint optimization of sonar waveform selection and sonobuoy placement. In 2023 SENSOR SIGNAL PROCESSING FOR DEFENCE CONFERENCE, SSPD (pp. 86-90). doi:10.1109/SSPD57945.2023.10256991

DOI
10.1109/SSPD57945.2023.10256991
Conference Paper

2022

The Design and Operation of a New Relativistic Ultrafast Electron Diffraction and Imaging (RUEDI) National Facility in the UK

Browning, N. D., Bryan, W., Clarke, J., Ellis, M., Kirkland, A. I., Maskell, S., . . . Welsch, C. (2022). The Design and Operation of a New Relativistic Ultrafast Electron Diffraction and Imaging (RUEDI) National Facility in the UK. Microscopy and Microanalysis, 28(S1), 2764-2765. doi:10.1017/s1431927622010406

DOI
10.1017/s1431927622010406
Journal article

Gaussian trajectory PMBM filter with nonlinear measurements based on posterior linearisation

Garcia-Fernandez, A. F., Ralph, J., Horridge, P., & Maskell, S. (2022). Gaussian trajectory PMBM filter with nonlinear measurements based on posterior linearisation. In 2022 25TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2022). Retrieved from https://www.webofscience.com/

Conference Paper

Inference of Stochastic Disease Transmission Models Using Particle-MCMC and a Gradient Based Proposal

Rosato, C., Harris, J., Panovska-Griffiths, J., & Maskell, S. (2022). Inference of Stochastic Disease Transmission Models Using Particle-MCMC and a Gradient Based Proposal. In 2022 25TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2022). Retrieved from https://www.webofscience.com/

Conference Paper

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

Barr, J., Harrald, O., Hiscocks, S., Perree, N., Pritchett, H., Vidal, S., . . . Vladimirov, L. (2022). Stone Soup open source framework for tracking and state estimation: enhancements and applications. In SIGNAL PROCESSING, SENSOR/INFORMATION FUSION, AND TARGET RECOGNITION XXXI Vol. 12122. doi:10.1117/12.2618495

DOI
10.1117/12.2618495
Conference Paper

Inference of Stochastic Disease Transmission Models Using Particle-MCMC and a Gradient Based Proposal

DOI
10.48550/arxiv.2205.07356
Preprint

Similarity and Consistency Assessment of Three Major Online Drug-Drug Interaction Resources

Kontsioti, E., Maskell, S., Bensalem, A., Dutta, B., & Pirmohamed, M. (2022). Similarity and Consistency Assessment of Three Major Online Drug-Drug Interaction Resources. British Journal of Clinical Pharmacology. doi:10.1111/bcp.15341

DOI
10.1111/bcp.15341
Journal article

Ensemble Kalman filter based sequential Monte Carlo sampler for sequential Bayesian inference

Wu, J., Wen, L., Green, P. L., Li, J., & Maskell, S. (2022). Ensemble Kalman filter based sequential Monte Carlo sampler for sequential Bayesian inference. STATISTICS AND COMPUTING, 32(1). doi:10.1007/s11222-021-10075-x

DOI
10.1007/s11222-021-10075-x
Journal article

Classical Tracking for Quantum Trajectories

Ralph, J. F., Maskell, S., Ransom, M., & Ulbricht, H. (2022). Classical Tracking for Quantum Trajectories. In IEEE 24th International Conference on Information Fusion (FUSION), 2021, pp. 1-8. Retrieved from http://arxiv.org/abs/2202.00276v1

Conference Paper

GroundsWell: Community-engaged and data-informed systems transformation of Urban Green and Blue Space for population health - a new initiative.

Hunter, R. F., Rodgers, S. E., Hilton, J., Clarke, M., Garcia, L., Ward Thompson, C., . . . GroundsWell Consortium. (2022). GroundsWell: Community-engaged and data-informed systems transformation of Urban Green and Blue Space for population health - a new initiative.. Wellcome open research, 7, 237. doi:10.12688/wellcomeopenres.18175.1

DOI
10.12688/wellcomeopenres.18175.1
Journal article

Optimizing sonobuoy placement using multiobjective machine learning

Taylor, C. M., Maskell, S., & Ralph, J. F. (2022). Optimizing sonobuoy placement using multiobjective machine learning. In 2022 Sensor Signal Processing for Defence Conference (SSPD) (pp. 1-5). IEEE. doi:10.1109/sspd54131.2022.9896216

DOI
10.1109/sspd54131.2022.9896216
Conference Paper

The DynAIRx Project Protocol: Artificial Intelligence for dynamic prescribing optimisation and care integration in multimorbidity.

Walker, L. E., Abuzour, A. S., Bollegala, D., Clegg, A., Gabbay, M., Griffiths, A., . . . Buchan, I. (2022). The DynAIRx Project Protocol: Artificial Intelligence for dynamic prescribing optimisation and care integration in multimorbidity.. Journal of multimorbidity and comorbidity, 12, 26335565221145493. doi:10.1177/26335565221145493

DOI
10.1177/26335565221145493
Journal article

2021

Fusing Low-Latency Data Feeds with Death Data to Accurately Nowcast COVID-19 Related Deaths

DOI
10.48550/arxiv.2112.08097
Preprint

An analysis on metric-driven multi-target sensor management: GOSPA versus OSPA

Garcia-Fernandez, A. F., Hernandez, M., & Maskell, S. (2021). An analysis on metric-driven multi-target sensor management: GOSPA versus OSPA. In 2021 IEEE 24th International Conference on Information Fusion (FUSION). IEEE. doi:10.23919/fusion49465.2021.9626837

DOI
10.23919/fusion49465.2021.9626837
Conference Paper

Classical Tracking for Quantum Trajectories

Ralph, J. F., Maskell, S., Ransom, M., & Ulbricht, H. (2021). Classical Tracking for Quantum Trajectories. In 2021 IEEE 24th International Conference on Information Fusion (FUSION) (pp. 1-8). IEEE. doi:10.23919/fusion49465.2021.9626966

DOI
10.23919/fusion49465.2021.9626966
Conference Paper

Posterior Cramér-Rao Bounds for Tracking Intermittently Visible Targets in Clutter

Hernandez, M. L., Ransom, M. J., & Maskell, S. (2021). Posterior Cramér-Rao Bounds for Tracking Intermittently Visible Targets in Clutter. In 2021 IEEE 24th International Conference on Information Fusion (FUSION) (pp. 1-8). IEEE. doi:10.23919/fusion49465.2021.9626856

DOI
10.23919/fusion49465.2021.9626856
Conference Paper

SMC samplers for Bayesian Optimisation and Discovery of Additive Kernel Structure

Chatzopoulou, A., Garcia-Fernandez, A. F., Pyzer-Knapp, E., & Maskell, S. (2021). SMC samplers for Bayesian Optimisation and Discovery of Additive Kernel Structure. In 2021 IEEE 24th International Conference on Information Fusion (FUSION) (pp. 1-8). IEEE. doi:10.23919/fusion49465.2021.9626877

DOI
10.23919/fusion49465.2021.9626877
Conference Paper

Track-before-detect Bernoulli filters for combining passive and active sensors

Ransom, M. J., Hernandez, M. L., Ralph, J. F., & Maskell, S. (2021). Track-before-detect Bernoulli filters for combining passive and active sensors. In 2021 IEEE 24th International Conference on Information Fusion (FUSION) (pp. 1-8). IEEE. doi:10.23919/fusion49465.2021.9626922

DOI
10.23919/fusion49465.2021.9626922
Conference Paper

A Gaussian Filtering Method for Multitarget Tracking With Nonlinear/Non-Gaussian Measurements

Garcia-Fernandez, A. F., Ralph, J., Horridge, P., & Maskell, S. (2021). A Gaussian Filtering Method for Multitarget Tracking With Nonlinear/Non-Gaussian Measurements. IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 57(5), 3539-3548. doi:10.1109/TAES.2021.3074200

DOI
10.1109/TAES.2021.3074200
Journal article

The No-U-Turn Sampler as a Proposal Distribution in a Sequential Monte Carlo Sampler with a Near-Optimal L-Kernel

DOI
10.48550/arxiv.2108.02498
Preprint

Increasing the efficiency of Sequential Monte Carlo samplers through the use of approximately optimal L-kernels

Green, P., Devlin, L., Moore, R., Jackson, R., Li, J., & Maskell, S. (2021). Increasing the efficiency of Sequential Monte Carlo samplers through the use of approximately optimal L-kernels. Mechanical Systems and Signal Processing. doi:10.1016/j.ymssp.2021.108028

DOI
10.1016/j.ymssp.2021.108028
Journal article

A Psychology-Driven Computational Analysis of Political Interviews

Cook, D., Zilka, M., Maskell, S., & Alison, L. (2021). A Psychology-Driven Computational Analysis of Political Interviews. In INTERSPEECH 2021 (pp. 1942-1946). doi:10.21437/Interspeech.2021-2249

DOI
10.21437/Interspeech.2021-2249
Conference Paper

A Psychology-Driven Computational Analysis of Political Interviews.

Cook, D., Zilka, M., Maskell, S., & Alison, L. (2021). A Psychology-Driven Computational Analysis of Political Interviews.. In H. Hermansky, H. Cernocký, L. Burget, L. Lamel, O. Scharenborg, & P. Motlícek (Eds.), Interspeech (pp. 1942-1946). ISCA. Retrieved from https://doi.org/10.21437/Interspeech.2021

Conference Paper

An analysis on metric-driven multi-target sensor management: GOSPA versus OSPA

Garcia-Fernandez, A. F., Hernandez, M., & Maskell, S. (2021). An analysis on metric-driven multi-target sensor management: GOSPA versus OSPA. In 2021 IEEE 24TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) (pp. 355-362). Retrieved from https://www.webofscience.com/

Conference Paper

Classical Tracking for Quantum Trajectories

Ralph, J. F., Maskell, S., Ransom, M., & Ulbricht, H. (2021). Classical Tracking for Quantum Trajectories. In 2021 IEEE 24TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) (pp. 869-876). Retrieved from https://www.webofscience.com/

Conference Paper

Early Predictor for Student Success Based on Behavioural and Demographical Indicators

Drousiotis, E., Shi, L., & Maskell, S. (2021). Early Predictor for Student Success Based on Behavioural and Demographical Indicators. In INTELLIGENT TUTORING SYSTEMS (ITS 2021) Vol. 12677 (pp. 161-172). doi:10.1007/978-3-030-80421-3_19

DOI
10.1007/978-3-030-80421-3_19
Conference Paper

Efficient Learning of the Parameters of Non-Linear Models using Differentiable Resampling in Particle Filters.

Preprint

Posterior Cramer-Rao Bounds for Tracking Intermittently Visible Targets in Clutter

Hernandez, M. L., Ransom, M. J., & Maskell, S. (2021). Posterior Cramer-Rao Bounds for Tracking Intermittently Visible Targets in Clutter. In 2021 IEEE 24TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) (pp. 484-491). Retrieved from https://www.webofscience.com/

Conference Paper

SMC samplers for Bayesian Optimisation and Discovery of Additive Kernel Structure

Chatzopoulou, A., Garcia-Fernandez, A. F., Pyzer-Knapp, E., & Maskell, S. (2021). SMC samplers for Bayesian Optimisation and Discovery of Additive Kernel Structure. In 2021 IEEE 24TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) (pp. 137-144). Retrieved from https://www.webofscience.com/

Conference Paper

Track-before-detect Bernoulli filters for combining passive and active sensors

Ransom, M. J., Hernandez, M. L., Ralph, J. F., & Maskell, S. (2021). Track-before-detect Bernoulli filters for combining passive and active sensors. In 2021 IEEE 24TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) (pp. 885-892). Retrieved from https://www.webofscience.com/

Conference Paper

2020

Ensemble Kalman filter based Sequential Monte Carlo Sampler for sequential Bayesian inference

DOI
10.48550/arxiv.2012.08848
Preprint

Welcome to the first issue of <scp><i>Applied AI Letters</i></scp>

Pyzer‐Knapp, E. O., Cuff, J., Patterson, J., Isayev, O., & Maskell, S. (2020). Welcome to the first issue of <scp><i>Applied AI Letters</i></scp>. Applied AI Letters, 1(1). doi:10.1002/ail2.8

DOI
10.1002/ail2.8
Journal article

A SMC Sampler for Joint Tracking and Destination Estimation from Noisy Data

Vladimirov, L., & Maskell, S. (2020). A SMC Sampler for Joint Tracking and Destination Estimation from Noisy Data. In PROCEEDINGS OF 2020 23RD INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2020) (pp. 1108-1115). Retrieved from https://www.webofscience.com/

Conference Paper

Continuous-discrete trajectory PHD and CPHD filters

Garcia-Fernandez, A. F., & Maskell, S. (2020). Continuous-discrete trajectory PHD and CPHD filters. In 2020 IEEE 23rd International Conference on Information Fusion (FUSION) (pp. 1-8). IEEE. doi:10.23919/fusion45008.2020.9190298

DOI
10.23919/fusion45008.2020.9190298
Conference Paper

Integrated Expected Likelihood Particle Filters

Ransom, M. J., Vladimirov, L., Horridge, P. R., Ralph, J. F., & Maskell, S. (2020). Integrated Expected Likelihood Particle Filters. In 2020 IEEE 23rd International Conference on Information Fusion (FUSION) (pp. 1-8). IEEE. doi:10.23919/fusion45008.2020.9190387

DOI
10.23919/fusion45008.2020.9190387
Conference Paper

Robust and Efficient Image Alignment Method Using the Student-t Distribution

Zhou, Y., & Maskell, S. (2020). Robust and Efficient Image Alignment Method Using the Student-t Distribution. In PROCEEDINGS OF 2020 23RD INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2020) (pp. 1255-1262). Retrieved from https://www.webofscience.com/

Conference Paper

Increasing the efficiency of Sequential Monte Carlo samplers through the use of approximately optimal L-kernels

DOI
10.48550/arxiv.2004.12838
Preprint

Continuous-discrete multiple target filtering: PMBM, PHD and CPHD filter implementations

Garcia-Fernandez, A., & Maskell, S. (2020). Continuous-discrete multiple target filtering: PMBM, PHD and CPHD filter implementations. Continuous-discrete multiple target filtering: PMBM, PHD and CPHD filter implementations. doi:10.1109/TSP.2020.2968247

DOI
10.1109/TSP.2020.2968247
Journal article

Bernoulli merging for the Poisson multi-Bernoulli mixture filter

Fontana, M., Garcia-Fernandez, A. F., & Maskell, S. (2020). Bernoulli merging for the Poisson multi-Bernoulli mixture filter. In PROCEEDINGS OF 2020 23RD INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2020) (pp. 262-269). Retrieved from https://www.webofscience.com/

Conference Paper

Continuous-discrete trajectory PHD and CPHD filters

Garcia-Fernandez, A. F., & Maskell, S. (2020). Continuous-discrete trajectory PHD and CPHD filters. In PROCEEDINGS OF 2020 23RD INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2020) (pp. 305-312). doi:10.23919/fusion45008.2020.9190298

DOI
10.23919/fusion45008.2020.9190298
Conference Paper

Integrated Expected Likelihood Particle Filters

Ransom, M. J., Vladimirov, L., Horridge, P. R., Ralph, J. F., & Maskell, S. (2020). Integrated Expected Likelihood Particle Filters. In PROCEEDINGS OF 2020 23RD INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2020) (pp. 849-856). Retrieved from https://www.webofscience.com/

Conference Paper

Weather Effects on Obstacle Detection for Autonomous Car

Song, R., Wetherall, J., Maskell, S., & Ralph, J. (2020). Weather Effects on Obstacle Detection for Autonomous Car. In Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems (pp. 331-341). SCITEPRESS - Science and Technology Publications. doi:10.5220/0009354500002550

DOI
10.5220/0009354500002550
Conference Paper

2019

Detecting and Tracking Small Moving Objects in Wide Area Motion Imagery (WAMI) Using Convolutional Neural Networks (CNNs)

DOI
10.48550/arxiv.1911.01727
Preprint

A Generic Anomaly Detection Approach Applied to Mixture-of-unigrams and Maritime Surveillance Data

Zhou, Y., Wright, J., & Maskell, S. (2019). A Generic Anomaly Detection Approach Applied to Mixture-of-unigrams and Maritime Surveillance Data. In 2019 SYMPOSIUM ON SENSOR DATA FUSION: TRENDS, SOLUTIONS, APPLICATIONS (SDF 2019). doi:10.1109/sdf.2019.8916633

DOI
10.1109/sdf.2019.8916633
Conference Paper

Detecting and Tracking Small Moving Objects in Wide Area Motion Imagery (WAMI) Using Convolutional Neural Networks (CNNs)

Zhou, Y., & Maskell, S. (2019). Detecting and Tracking Small Moving Objects in Wide Area Motion Imagery (WAMI) Using Convolutional Neural Networks (CNNs). In 2019 22ND INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2019). doi:10.23919/fusion43075.2019.9011271

DOI
10.23919/fusion43075.2019.9011271
Conference Paper

Recommendations on the Use of Mobile Applications for the Collection and Communication of Pharmaceutical Product Safety Information: Lessons from IMI WEB-RADR

Pierce, C. E., de Vries, S. T., Bodin-Parssinen, S., Harmark, L., Tregunno, P., Lewis, D. J., . . . Mol, P. G. M. (2019). Recommendations on the Use of Mobile Applications for the Collection and Communication of Pharmaceutical Product Safety Information: Lessons from IMI WEB-RADR. DRUG SAFETY, 42(4), 477-489. doi:10.1007/s40264-019-00813-6

DOI
10.1007/s40264-019-00813-6
Journal article

2018

Convolutional Neural Networks for Aerial Vehicle Detection and Recognition

Soleimani, A., Nasrabadi, N. M., Griffith, E., Ralph, J., & Maskell, S. (2018). Convolutional Neural Networks for Aerial Vehicle Detection and Recognition. In NAECON 2018 - IEEE NATIONAL AEROSPACE AND ELECTRONICS CONFERENCE (pp. 186-191). Retrieved from https://www.webofscience.com/

Conference Paper

Assessment of the Utility of Social Media for Broad-Ranging Statistical Signal Detection in Pharmacovigilance: Results from the WEB-RADR Project.

Caster, O., Dietrich, J., Kuerzinger, M. -L., Lerch, M., Maskell, S., Noren, G. N., . . . van Stekelenborg, J. (2018). Assessment of the Utility of Social Media for Broad-Ranging Statistical Signal Detection in Pharmacovigilance: Results from the WEB-RADR Project. DRUG SAFETY, 41(12), 1355-1369. doi:10.1007/s40264-018-0699-2

DOI
10.1007/s40264-018-0699-2
Journal article

Comparing interrelationships between features and embedding methods for multiple-view fusion

Piroddi, R., Goulermas, Y., Maskell, S., & Ralph, J. (2018). Comparing interrelationships between features and embedding methods for multiple-view fusion. In 2018 21ST INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) (pp. 1503-1510). Retrieved from https://www.webofscience.com/

Conference Paper

Fusing Bearing-only Measurements With and Without Propagation Delays Using Particle Trajectories

Horridge, P., & Maskell, S. (2018). Fusing Bearing-only Measurements With and Without Propagation Delays Using Particle Trajectories. In 2018 21ST INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) (pp. 989-996). Retrieved from https://www.webofscience.com/

Conference Paper

Langevin Incremental Mixture Importance Sampling

Fasiolo, M., de melo, F., & Maskell, S. (2018). Langevin Incremental Mixture Importance Sampling. Statistics and Computing, 28, 549-561. doi:10.1007/s11222-017-9747-5

DOI
10.1007/s11222-017-9747-5
Journal article

2017

Moving Object Detection Using Background Subtraction for a Moving Camera with Pronounced Parallax

Zhou, Y., & Maskell, S. (2017). Moving Object Detection Using Background Subtraction for a Moving Camera with Pronounced Parallax. In 2017 SENSOR DATA FUSION: TRENDS, SOLUTIONS, APPLICATIONS (SDF). Retrieved from https://www.webofscience.com/

Conference Paper

Multiparameter estimation along quantum trajectories with sequential Monte Carlo methods

Ralph, J. F., Maskell, S., & Kacobs, K. (2017). Multiparameter estimation along quantum trajectories with sequential Monte Carlo methods. Physical Review A (Atomic, Molecular and Optical Physics), 96. doi:10.1103/PhysRevA.96.052306

DOI
10.1103/PhysRevA.96.052306
Journal article

Beyond co-occurrence-based ADR detection from Social Media

Bollegala, D., Maskell, S., & Pirmohamed, M. (2017). Beyond co-occurrence-based ADR detection from Social Media. Poster session presented at the meeting of Unknown Conference. Retrieved from https://www.webofscience.com/

Poster

Estimating the parameters of dynamical systems from Big Data using Sequential Monte Carlo samplers

Green, P. L., & Maskell, S. (2017). Estimating the parameters of dynamical systems from Big Data using Sequential Monte Carlo samplers. Mechanical Systems and Signal Processing, 93, 379-396. doi:10.1016/j.ymssp.2016.12.023

DOI
10.1016/j.ymssp.2016.12.023
Journal article

Nonlinear Kinematics for Improved Helicopter Tracking

Clark, E. J., Griffith, E. J., Maskell, S., & Ralph, J. F. (2017). Nonlinear Kinematics for Improved Helicopter Tracking. In 2017 20TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) (pp. 1013-1018). Retrieved from https://www.webofscience.com/

Conference Paper

RB<sup>2</sup>— PF : A novel filter-based monocular visual odometry algorithm

Zhou, Y., & Maskell, S. (2017). RB<sup>2</sup>— PF : A novel filter-based monocular visual odometry algorithm. In 2017 20th International Conference on Information Fusion (Fusion) (pp. 1-8). IEEE. doi:10.23919/icif.2017.8009745

DOI
10.23919/icif.2017.8009745
Conference Paper

Multi-parameter estimation along quantum trajectories with Sequential Monte Carlo methods

DOI
10.48550/arxiv.1707.04725
Preprint

Causality Patterns for Detecting Adverse Drug Reactions From Social Media: Text Mining Approach (Preprint)

Bollegala, D., Maskell, S., Sloane, R., Hajne, J., & Pirmohamed, M. (2017). Causality Patterns for Detecting Adverse Drug Reactions From Social Media: Text Mining Approach (Preprint). doi:10.2196/preprints.8214

DOI
10.2196/preprints.8214
Journal article

<i>RB</i><SUP>2</SUP> - <i>PF</i>: A Novel Filter-based Monocular Visual Odometry Algorithm

Zhou, Y., & Maskell, S. (2017). <i>RB</i><SUP>2</SUP> - <i>PF</i>: A Novel Filter-based Monocular Visual Odometry Algorithm. In 2017 20TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) (pp. 897-904). Retrieved from https://www.webofscience.com/

Conference Paper

An intersection-centric auction-based traffic signal control framework

Raphael, J., Sklar, E. I., & Maskell, S. (2017). An Intersection-Centric Auction-Based Traffic Signal Control Framework. In AGENT-BASED MODELING OF SUSTAINABLE BEHAVIORS (pp. 121-142). doi:10.1007/978-3-319-46331-5_6

DOI
10.1007/978-3-319-46331-5_6
Chapter

Estimating the Pertinent Information Present in Social Media, not just what an Algorithm Detects

Maskell, S., Sloane, R., Perkins, S., Heap, J., Hajne, J., Jones, A., & Pirmohamed, M. (2017). Estimating the Pertinent Information Present in Social Media, not just what an Algorithm Detects. Poster session presented at the meeting of Unknown Conference. Retrieved from https://www.webofscience.com/

Poster

Looking Longitudinally in Twitter: Reading More than 140 Characters

Maskell, S., Sloane, R., Hajne, J., Heap, J., Perkins, S., Griffith, E., . . . Pirmohamed, M. (2017). Looking Longitudinally in Twitter: Reading More than 140 Characters. Poster session presented at the meeting of Unknown Conference. Retrieved from https://www.webofscience.com/

Poster

Parallelising Particle Filters with Deterministic Runtime on Distributed Memory Systems

Varsi, A., Kekempanos, L., Thiyagalingam, J., & Maskell, S. (2017). Parallelising Particle Filters with Deterministic Runtime on Distributed Memory Systems. In IET 3rd International Conference on Intelligent Signal Processing (ISP 2017) (pp. 11 (10 .)). Institution of Engineering and Technology. doi:10.1049/cp.2017.0357

DOI
10.1049/cp.2017.0357
Conference Paper

2016

Geometric Separation of Superimposed Images

Mehta, M. M., Griffith, E. J., Maskell, S., & Ralph, J. F. (2016). Geometric Separation of Superimposed Images. In 2016 19TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) (pp. 1244-1251). Retrieved from https://www.webofscience.com/

Conference Paper

Using a Bayesian Model for Confidence to Make Decisions that Consider Epistemic Regret

Anderson, R., Hare, N., & Maskell, S. (2016). Using a Bayesian Model for Confidence to Make Decisions that Consider Epistemic Regret. In 2016 19TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) (pp. 264-269). Retrieved from https://www.webofscience.com/

Conference Paper

Efficient estimation of probability of conflict between air traffic using Subset Simulation

DOI
10.48550/arxiv.1604.07363
Preprint

An empirical investigation of adaptive traffic control parameters

Raphael, J., Sklar, E. I., & Maskell, S. (2016). An empirical investigation of adaptive traffic control parameters. In CEUR Workshop Proceedings Vol. 1678.

Conference Paper

Parameter estimation from big data using a sequential monte carlo sampler

Green, P. L., & Maskell, S. (2016). Parameter estimation from big data using a sequential monte carlo sampler. In PROCEEDINGS OF ISMA2016 INTERNATIONAL CONFERENCE ON NOISE AND VIBRATION ENGINEERING AND USD2016 INTERNATIONAL CONFERENCE ON UNCERTAINTY IN STRUCTURAL DYNAMICS (pp. 4111-4119). Retrieved from https://www.webofscience.com/

Conference Paper

2015

Datasets reflecting students' and teachers' views on the use of learning technology in a UK university

Limniou, M., Downes, J. J., & Maskell, S. (2015). Datasets reflecting students' and teachers' views on the use of learning technology in a UK university. BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY, 46(5), 1081-1091. doi:10.1111/bjet.12332

DOI
10.1111/bjet.12332
Journal article

First Steps Toward an Auction-Based Traffic Signal Controller

Raphael, J., Maskell, S., & Sklar, E. (2015). First Steps Toward an Auction-Based Traffic Signal Controller. In ADVANCES IN PRACTICAL APPLICATIONS OF AGENTS, MULTI-AGENT SYSTEMS, AND SUSTAINABILITY Vol. 9086 (pp. 300-303). doi:10.1007/978-3-319-18944-4_32

DOI
10.1007/978-3-319-18944-4_32
Conference Paper

From Goods to Traffic: First Steps Toward an Auction-Based Traffic Signal Controller

Raphael, J., Maskell, S., & Sklar, E. (2015). From Goods to Traffic: First Steps Toward an Auction-Based Traffic Signal Controller. In ADVANCES IN PRACTICAL APPLICATIONS OF AGENTS, MULTI-AGENT SYSTEMS, AND SUSTAINABILITY Vol. 9086 (pp. 187-198). doi:10.1007/978-3-319-18944-4_16

DOI
10.1007/978-3-319-18944-4_16
Conference Paper

2014

Probabilistic graphical detector fusion for localization of faces and facial parts

Liu, C. Y., Zhou, Y., de Melo, F., & Maskell, S. (2014). Probabilistic graphical detector fusion for localization of faces and facial parts. In 2014 Sensor Data Fusion: Trends, Solutions, Applications (SDF) (pp. 1-6). IEEE. doi:10.1109/sdf.2014.6954708

DOI
10.1109/sdf.2014.6954708
Conference Paper

Efficient Data Structures for Large Scale Tracking

Lane, R. O., Briers, M., Cooper, T. M., & Maskell, S. R. (2014). Efficient Data Structures for Large Scale Tracking. In 2014 17TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION). Retrieved from https://www.webofscience.com/

Conference Paper

Hybrid Gauss-Hermite Filter

de Melo, F. E., & Maskell, S. (2014). Hybrid Gauss-Hermite Filter. In IET Conference on Data Fusion &amp; Target Tracking 2014: Algorithms and Applications (pp. 4.1). Institution of Engineering and Technology. doi:10.1049/cp.2014.0530

DOI
10.1049/cp.2014.0530
Conference Paper

2013

Optimised Proposals for Improved Propagation of Multi-Modal Distributions in Particle Filters

Maskell, S., & Julier, S. (2013). Optimised Proposals for Improved Propagation of Multi-Modal Distributions in Particle Filters. In 2013 16TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) (pp. 296-303). Retrieved from https://www.webofscience.com/

Conference Paper

Articulated human body parts detection based on cluster background subtraction and foreground matching

Bhaskar, H., Mihaylova, L., & Maskell, S. (2013). Articulated human body parts detection based on cluster background subtraction and foreground matching. Neurocomputing, 100, 58-73. doi:10.1016/j.neucom.2011.12.039

DOI
10.1016/j.neucom.2011.12.039
Journal article

2012

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

Kent, P., Maskell, S., Payne, O., Richardson, S., & Scarff, L. (2012). Robust background subtraction for automated detection and tracking of targets in wide area motion imagery. In C. Lewis, & D. Burgess (Eds.), SPIE Proceedings Vol. 8546 (pp. 85460Q). SPIE. doi:10.1117/12.965300

DOI
10.1117/12.965300
Conference Paper

Maneuvering target tracking using an unbiased nearly constant heading model

Kountouriotis, P. A., & Maskell, S. (2012). Maneuvering target tracking using an unbiased nearly constant heading model. In 15th International Conference on Information Fusion, FUSION 2012 (pp. 2249-2255).

Conference Paper

An application of sequential Monte Carlo samplers: an alternative to particle filters for non-linear non-gaussian sequential inference with zero process noise

Maskell, S. (2012). An application of sequential Monte Carlo samplers: an alternative to particle filters for non-linear non-gaussian sequential inference with zero process noise. In 9th IET Data Fusion &amp; Target Tracking Conference (DF&amp;TT 2012): Algorithms &amp; Applications (pp. 13). IET. doi:10.1049/cp.2012.0413

DOI
10.1049/cp.2012.0413
Conference Paper

2011

Group Object Structure and State Estimation With Evolving Networks and Monte Carlo Methods

Gning, A., Mihaylova, L., Maskell, S., Pang, S. K., & Godsill, S. (2011). Group Object Structure and State Estimation With Evolving Networks and Monte Carlo Methods. IEEE Transactions on Signal Processing, 59(4), 1383-1396. doi:10.1109/tsp.2010.2103062

DOI
10.1109/tsp.2010.2103062
Journal article

2010

Welcome to fusion 2010, the 13th International conference on information fusion and welcome to Edinburgh

Maskell, S. (2010). Welcome to fusion 2010, the 13th International conference on information fusion and welcome to Edinburgh. In 13th Conference on Information Fusion, Fusion 2010 (pp. 4-5).

Conference Paper

Smoothing algorithms for state–space models

Briers, M., Doucet, A., & Maskell, S. (2010). Smoothing algorithms for state–space models. Annals of the Institute of Statistical Mathematics, 62(1), 61-89. doi:10.1007/s10463-009-0236-2

DOI
10.1007/s10463-009-0236-2
Journal article

A Bayesian approach to joint tracking and identification of geometric shapes in video sequences

Minvielle, P., Doucet, A., Marrs, A., & Maskell, S. (2010). A Bayesian approach to joint tracking and identification of geometric shapes in video sequences. Image and Vision Computing, 28(1), 111-123. doi:10.1016/j.imavis.2009.05.002

DOI
10.1016/j.imavis.2009.05.002
Journal article

Fusion of data from sources with different levels of trust

Nevell, D. A., Maskell, S. R., Horridge, P. R., & Barnett, H. L. (2010). Fusion of data from sources with different levels of trust. In 2010 13th International Conference on Information Fusion (pp. 1-7). IEEE. doi:10.1109/icif.2010.5711842

DOI
10.1109/icif.2010.5711842
Conference Paper

2009

A scalable method of tracking targets with dependent distributions

Horridge, P., & Maskell, S. (2009). A scalable method of tracking targets with dependent distributions. In 2009 12th International Conference on Information Fusion, FUSION 2009 (pp. 603-610).

Conference Paper

Searching for, initiating and tracking multiple targets using existence probabilities

Horridge, P., & Maskell, S. (2009). Searching for, initiating and tracking multiple targets using existence probabilities. In 2009 12th International Conference on Information Fusion, FUSION 2009 (pp. 611-617).

Conference Paper

Statistical Methods for Target Tracking

Maskell, S. (n.d.). Statistical Methods for Target Tracking. In Unknown Book (pp. 2820-2829). Wiley. doi:10.1002/9780470050118.ecse645

DOI
10.1002/9780470050118.ecse645
Chapter

2008

Evolving networks for group object motion estimation

Gning, A., Mihaylova, L., Maskell, S., Sze Kim Pang., & Godsill, S. (2008). Evolving networks for group object motion estimation. In IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications (pp. 97-106). IEE. doi:10.1049/ic:20080061

DOI
10.1049/ic:20080061
Conference Paper

Ground target group structure and state estimation with particle filtering

Gning, A., Mihaylova, L., Maskell, S., Sze, K. P., & Godsill, S. (2008). Ground target group structure and state estimation with particle filtering. In Proceedings of the 11th International Conference on Information Fusion, FUSION 2008. doi:10.1109/ICIF.2008.4632343

DOI
10.1109/ICIF.2008.4632343
Conference Paper

Human body parts tracking using pictorial structures and a genetic algorithm

Bhaskar, H., Mihaylova, L., & Maskell, S. (2008). Human body parts tracking using pictorial structures and a genetic algorithm. In 2008 4th International IEEE Conference Intelligent Systems. IEEE. doi:10.1109/is.2008.4670489

DOI
10.1109/is.2008.4670489
Conference Paper

Population based particle filtering

Bhaskar, H., Mihaylova, L., & Maskell, S. (2008). Population based particle filtering. In IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications (pp. 29-38). IEE. doi:10.1049/ic:20080054

DOI
10.1049/ic:20080054
Conference Paper

Tracking with inter-visibility variables

Horridge, P., & Maskell, S. (2008). Tracking with inter-visibility variables. In IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications (pp. 59-68). IEE. doi:10.1049/ic:20080057

DOI
10.1049/ic:20080057
Conference Paper

Using ship tracking methods to assist in quality controlling and bias adjusting meteorological observations in a marine environment

Hill, J. G. T., Maskelf, S. R., & Cole, M. (2008). Using ship tracking methods to assist in quality controlling and bias adjusting meteorological observations in a marine environment. In IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications (pp. 167-174). IEE. doi:10.1049/ic:20080069

DOI
10.1049/ic:20080069
Conference Paper

A Bayesian approach to fusing uncertain, imprecise and conflicting information

Maskell, S. (2008). A Bayesian approach to fusing uncertain, imprecise and conflicting information. Information Fusion, 9(2), 259-277. doi:10.1016/j.inffus.2007.02.003

DOI
10.1016/j.inffus.2007.02.003
Journal article

2007

Background modeling using adaptive cluster density estimation for automatic human detection

Bhaskar, H., Mihaylova, L., & Maskell, S. (2007). Background modeling using adaptive cluster density estimation for automatic human detection. In INFORMATIK 2007 - Informatik Trifft Logistik, Beitrage der 37. Jahrestagung der Gesellschaft fur Informatik e.V. (GI) Vol. 2 (pp. 130-134).

Conference Paper

A Tutorial on Particle Filters for Online Nonlinear/NonGaussian Bayesian Tracking

A Tutorial on Particle Filters for Online Nonlinear/NonGaussian Bayesian Tracking (2009). In Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking. IEEE. doi:10.1109/9780470544198.ch73

DOI
10.1109/9780470544198.ch73
Chapter

2006

Distributed tracking of stealthy targets using particle filters

Maskell, S. R., Weekes, K. R., & Briers, M. (2006). Distributed tracking of stealthy targets using particle filters. In IEE Seminar on Target Tracking: Algorithms and Applications (pp. 17-26). IEE. doi:10.1049/ic:20060553

DOI
10.1049/ic:20060553
Conference Paper

Fast particle smoothing

Klaas, M., Briers, M., de Freitas, N., Doucet, A., Maskell, S., & Lang, D. (2006). Fast particle smoothing. In Proceedings of the 23rd international conference on Machine learning - ICML '06 (pp. 481-488). ACM Press. doi:10.1145/1143844.1143905

DOI
10.1145/1143844.1143905
Conference Paper

Joint Tracking and Classification of Airbourne Objects using Particle Filters and the Continuous Transferable Belief Model

Powell, G., Marshall, D., Smets, P., Ristic, B., & Maskell, S. (2006). Joint Tracking and Classification of Airbourne Objects using Particle Filters and the Continuous Transferable Belief Model. In 2006 9th International Conference on Information Fusion (pp. 1-8). IEEE. doi:10.1109/icif.2006.301718

DOI
10.1109/icif.2006.301718
Conference Paper

Real-Time Tracking Of Hundreds Of Targets With Efficient Exact JPDAF Implementation

Horridge, P., & Maskell, S. (2006). Real-Time Tracking Of Hundreds Of Targets With Efficient Exact JPDAF Implementation. In 2006 9th International Conference on Information Fusion (pp. 1-8). IEEE. doi:10.1109/icif.2006.301561

DOI
10.1109/icif.2006.301561
Conference Paper

Fast particle smoothing: If I had a million particles

Klaas, M., Briers, M., De Freitas, N., Doucet, A., Maskell, S., & Lang, D. (2006). Fast particle smoothing: If I had a million particles. In ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning Vol. 2006 (pp. 481-488).

Conference Paper

Fixed-lag sequential Monte Carlo data association

Briers, M., Doucet, A., Maskell, S. R., & Horridge, P. R. (2006). Fixed-lag sequential Monte Carlo data association. In O. E. Drummond (Ed.), SPIE Proceedings Vol. 6236 (pp. 62360S). SPIE. doi:10.1117/12.665684

DOI
10.1117/12.665684
Conference Paper

A Single Instruction Multiple Data Particle Filter

Maskell, S., Alun-Jones, B., & Macleod, M. (2006). A Single Instruction Multiple Data Particle Filter. In 2006 IEEE Nonlinear Statistical Signal Processing Workshop (pp. 51-54). IEEE. doi:10.1109/nsspw.2006.4378818

DOI
10.1109/nsspw.2006.4378818
Conference Paper

Multi-target out-of-sequence data association: Tracking using graphical models

Maskell, S. R., Everitt, R. G., Wright, R., & Briers, M. (2006). Multi-target out-of-sequence data association: Tracking using graphical models. Information Fusion, 7(4), 434-447. doi:10.1016/j.inffus.2005.07.001

DOI
10.1016/j.inffus.2005.07.001
Journal article

2005

Bayesian visual tracking with existence process

Vermaak, J., Maskell, S., Briers, M., & Perez, P. (2005). Bayesian visual tracking with existence process. In IEEE International Conference on Image Processing 2005 (pp. I-721). IEEE. doi:10.1109/icip.2005.1529852

DOI
10.1109/icip.2005.1529852
Conference Paper

How to best track a crowd?

Maskell, S., Briers, M., Everitt, R., & Horridge, P. (2005). How to best track a crowd?. In Proceedings of SPIE - The International Society for Optical Engineering Vol. 5809 (pp. 590-593).

Conference Paper

Recursive track-before-detect with target amplitude fluctuations

Rutten, M. G., Gordon, N. J., & Maskell, S. (2005). Recursive track-before-detect with target amplitude fluctuations. In IEE Proceedings - Radar, Sonar and Navigation Vol. 152 (pp. 345). Institution of Engineering and Technology (IET). doi:10.1049/ip-rsn:20045041

DOI
10.1049/ip-rsn:20045041
Conference Paper

Tracking using a radar and a problem specific proposal distribution in a particle filter

Maskell, S., Briers, M., Wright, R., & Horridge, P. (2005). Tracking using a radar and a problem specific proposal distribution in a particle filter. In IEE Proceedings - Radar, Sonar and Navigation Vol. 152 (pp. 315). Institution of Engineering and Technology (IET). doi:10.1049/ip-rsn:20045033

DOI
10.1049/ip-rsn:20045033
Conference Paper

Poisson models for extended target and group tracking

Gilholm, K., Godsill, S., Maskell, S., & Salmond, D. (2005). Poisson models for extended target and group tracking. In O. E. Drummond (Ed.), SPIE Proceedings. SPIE. doi:10.1117/12.618730

DOI
10.1117/12.618730
Conference Paper

A comparison of the particle and shifted Rayleigh filters in their application to a multisensor bearings-only problem

Clark, M., Maskell, S., Vinter, R., & Yaqoob, M. (2005). A comparison of the particle and shifted Rayleigh filters in their application to a multisensor bearings-only problem. In 2005 IEEE Aerospace Conference (pp. 2142-2147). IEEE. doi:10.1109/aero.2005.1559505

DOI
10.1109/aero.2005.1559505
Conference Paper

A unifying framework for multi-target tracking and existence

Vermaak, J., Maskell, S., & Briers, M. (2005). A unifying framework for multi-target tracking and existence. In 2005 7th International Conference on Information Fusion (pp. 9 pp.). IEEE. doi:10.1109/icif.2005.1591862

DOI
10.1109/icif.2005.1591862
Conference Paper

Joint target tracking and identification-Part I: sequential Monte Carlo model-based approaches

Minvielle, P., Marrs, A. D., Maskell, S., & Doucet, A. (2005). Joint target tracking and identification-Part I: sequential Monte Carlo model-based approaches. In 2005 7th International Conference on Information Fusion (pp. 8 pp.). IEEE. doi:10.1109/icif.2005.1591863

DOI
10.1109/icif.2005.1591863
Conference Paper

Joint target tracking and identification. Part II. Shape video computing

Minvielle, P., Marrs, A. D., Maskell, S., & Doucet, A. (2005). Joint target tracking and identification. Part II. Shape video computing. In 2005 7th International Conference on Information Fusion (pp. 8 pp.). IEEE. doi:10.1109/icif.2005.1591864

DOI
10.1109/icif.2005.1591864
Conference Paper

Online sensor registration

Vermaak, J., Maskell, S., & Briers, M. (2005). Online sensor registration. In 2005 IEEE Aerospace Conference (pp. 2117-2125). IEEE. doi:10.1109/aero.2005.1559503

DOI
10.1109/aero.2005.1559503
Conference Paper

2004

Efficient particle-based track-before-detect in rayleigh noise

Rutten, M. G., Gordon, N. J., & Maskell, S. (2004). Efficient particle-based track-before-detect in rayleigh noise. In Proceedings of the Seventh International Conference on Information Fusion, FUSION 2004 Vol. 2 (pp. 693-700).

Conference Paper

Multi-target out-of-sequence data association

Maskell, S., Everitt, R., Wright, R., & Briers, M. (2004). Multi-target out-of-sequence data association. In Proceedings of the Seventh International Conference on Information Fusion, FUSION 2004 Vol. 2 (pp. 1044-1051).

Conference Paper

Joint Tracking of Manoeuvring Targets and Classification of Their Manoeuvrability

Maskell, S. (n.d.). Joint Tracking of Manoeuvring Targets and Classification of Their Manoeuvrability. EURASIP Journal on Advances in Signal Processing, 2004(15). doi:10.1155/s1110865704404223

DOI
10.1155/s1110865704404223
Journal article

&lt;title&gt;Fast mutual exclusion&lt;/title&gt;

Maskell, S., Briers, M., & Wright, R. (2004). &lt;title&gt;Fast mutual exclusion&lt;/title&gt;. In O. E. Drummond (Ed.), SPIE Proceedings Vol. 5428 (pp. 526-536). SPIE. doi:10.1117/12.542068

DOI
10.1117/12.542068
Conference Paper

&lt;title&gt;Multipath track association for over-the-horizon radar using Lagrangian relaxation&lt;/title&gt;

Rutten, M. G., Maskell, S., Briers, M., & Gordon, N. J. (2004). &lt;title&gt;Multipath track association for over-the-horizon radar using Lagrangian relaxation&lt;/title&gt;. In O. E. Drummond (Ed.), SPIE Proceedings. SPIE. doi:10.1117/12.541276

DOI
10.1117/12.541276
Conference Paper

&lt;title&gt;Particle-based track-before-detect in Rayleigh noise&lt;/title&gt;

Rutten, M. G., Gordon, N. J., & Maskell, S. (2004). &lt;title&gt;Particle-based track-before-detect in Rayleigh noise&lt;/title&gt;. In O. E. Drummond (Ed.), SPIE Proceedings Vol. 5428 (pp. 509-519). SPIE. doi:10.1117/12.541275

DOI
10.1117/12.541275
Conference Paper

&lt;title&gt;Tracking maneuvering targets using a scale mixture of normals&lt;/title&gt;

Maskell, S., Gordon, N. J., Everett, N., & Robinson, M. (2004). &lt;title&gt;Tracking maneuvering targets using a scale mixture of normals&lt;/title&gt;. In O. E. Drummond (Ed.), SPIE Proceedings Vol. 5428 (pp. 134-144). SPIE. doi:10.1117/12.542071

DOI
10.1117/12.542071
Conference Paper

An introduction to particle filters

Maskell, S. (2004). An introduction to particle filters. In State Space and Unobserved Component Models (pp. 40-72). Cambridge University Press (CUP). doi:10.1017/cbo9780511617010.004

DOI
10.1017/cbo9780511617010.004
Chapter

An introduction to particle filters

Maskell, S. (2004). An introduction to particle filters. In State Space and Unobserved Component Models (pp. 40-72). Cambridge University Press. doi:10.1017/cbo9780511617010.004

DOI
10.1017/cbo9780511617010.004
Chapter

Distribution in a particle filter

Maskell, S. (2004). Distribution in a particle filter. In Target Tracking 2004: Algorithms and Applications Vol. 2004 (pp. 23-31). IEE. doi:10.1049/ic:20040048

DOI
10.1049/ic:20040048
Conference Paper

2003

&lt;title&gt;Two-dimensional assignment with merged measurements using Langrangrian relaxation&lt;/title&gt;

Briers, M., Maskell, S., & Philpott, M. (2003). &lt;title&gt;Two-dimensional assignment with merged measurements using Langrangrian relaxation&lt;/title&gt;. In O. E. Drummond (Ed.), SPIE Proceedings Vol. 5204 (pp. 283-292). SPIE. doi:10.1117/12.503834

DOI
10.1117/12.503834
Conference Paper

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

Maskell, S., Rollason, M., Gordon, N., & Salmond, D. (2003). Efficient particle filtering for multiple target tracking with application to tracking in structured images. In Image and Vision Computing Vol. 21 (pp. 931-939). Elsevier BV. doi:10.1016/s0262-8856(03)00087-8

DOI
10.1016/s0262-8856(03)00087-8
Conference Paper

A rao-blackwellised unscented Kalman filter

Briers, M., Maskell, S. R., & Wright, R. (2003). A rao-blackwellised unscented Kalman filter. In Sixth International Conference of Information Fusion, 2003. Proceedings of the (pp. 55-61). IEEE. doi:10.1109/icif.2003.177426

DOI
10.1109/icif.2003.177426
Conference Paper

2002

&lt;title&gt;Comparison of EKF, pseudomeasurement, and particle filters for a bearing-only target tracking problem&lt;/title&gt;

Lin, X., Kirubarajan, T., Bar-Shalom, Y., & Maskell, S. (2002). &lt;title&gt;Comparison of EKF, pseudomeasurement, and particle filters for a bearing-only target tracking problem&lt;/title&gt;. In O. E. Drummond (Ed.), SPIE Proceedings. SPIE. doi:10.1117/12.478508

DOI
10.1117/12.478508
Conference Paper

&lt;title&gt;Efficient particle filtering for multiple target tracking with application to tracking in structured images&lt;/title&gt;

Maskell, S., Rollason, M. P., Gordon, N. J., & Salmond, D. J. (2002). &lt;title&gt;Efficient particle filtering for multiple target tracking with application to tracking in structured images&lt;/title&gt;. In O. E. Drummond (Ed.), SPIE Proceedings Vol. 4728 (pp. 251-262). SPIE. doi:10.1117/12.478509

DOI
10.1117/12.478509
Conference Paper

&lt;title&gt;Efficient particle filters for joint tracking and classification&lt;/title&gt;

Gordon, N. J., Maskell, S., & Kirubarajan, T. (2002). &lt;title&gt;Efficient particle filters for joint tracking and classification&lt;/title&gt;. In O. E. Drummond (Ed.), SPIE Proceedings Vol. 4728 (pp. 439-449). SPIE. doi:10.1117/12.478524

DOI
10.1117/12.478524
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&lt;title&gt;Expected likelihood for tracking in clutter with particle filters&lt;/title&gt;

Marrs, A., Maskell, S., & Bar-Shalom, Y. (2002). &lt;title&gt;Expected likelihood for tracking in clutter with particle filters&lt;/title&gt;. In O. E. Drummond (Ed.), SPIE Proceedings Vol. 4728 (pp. 230-239). SPIE. doi:10.1117/12.478507

DOI
10.1117/12.478507
Conference Paper

A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking

Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 50(2), 174-188. doi:10.1109/78.978374

DOI
10.1109/78.978374
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2001

A tutorial on particle filters for on-line nonlinear/non-Gaussian Bayesian tracking

Maskell, S. (2001). A tutorial on particle filters for on-line nonlinear/non-Gaussian Bayesian tracking. IEE International Seminar Target Tracking: Algorithms and Applications, 2001. doi:10.1049/ic:20010246

DOI
10.1049/ic:20010246
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Undated

Notch Periodogram for Multiple Vehicle Trajectory Estimation with Distributed Acoustic Sensing

Fontana, M., García-Fernández, Á. F., & Maskell, S. (n.d.). Notch Periodogram for Multiple Vehicle Trajectory Estimation with Distributed Acoustic Sensing.

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