I'm currently on the look out for candidate PhD students to work on the following project:
If you are interested or know someone who might be, please get in touch.
Simon Maskell's Homepage
That's me looking rather happy and this is my website. You can contact me on: < s maskell liverpool ac uk >.
I'm a Professor of Autonomous Systems at the University of Liverpool within the School of Electical Engineering, Electronics and Computer Science where I am director of the Liverpool Big Data Network and am affiliated to both the Centre for Autonomous Systems and the Institute for Risk and Uncertainty. I teach "Control Theory" (to second year undergraduates) and a Big Data Analytics module as part of a new MSc on Big Data and High Performance Computing (being delivered in partnership with the UK centre for supercomputing at STFC's Hartree Centre). I have also historically taught "Image Processing" (to a mix of third and fourth year undergraduates and MSc students). My research team currently comprises:
- Richard Sloane (Post-Doctoral Research Assistant, working on WEB-RADR);
- Joanna Hajne (Post-Doctoral Research Assistant, working on WEB-RADR);
- Roberta Piroddi (Post-Doctoral Research Assistant, funded by EPSRC to work on Bayesian Analysis of Competing Cyber Hypotheses);
- Elias Griffith (Post-Doctoral Research Assistant, recently funded (via Roke Manor) by Dstl)
- Flávio de Melo (PhD student);
- Yifan Zhou (PhD student);
- Lykourgos Kekempanos (PhD student, funded by EPSRC);
- Chloe Barrett-Pink (PhD student, funded by Dstl, co-supervised with Laurence Alison as primary supervisor);
- Ross Anderson (PhD student, funded by EPSRC);
- Lyudmil Vladimirov (PhD student, EPSRC iCASE award with Denbridge Marine, facilitated by the Industrial Mathematics KTN (Knowledge Transfer Partnership));
- James Wright (PhD student, EPSRC iCASE award with Airbus);
- Alex Phillips (PhD student, funded by EPSRC);
- Alessandro Varsi (PhD student, EPSRC iCASE award with Schlumberger, co-supervised with Jeyan Thiyagalingam as primary supervisor);
- Oscar Martin-Camacho (PhD student, co-supervised with Andy Jones as primary supervisor);
- Frances Surmon-Bohr (PhD student, co-supervised with Laurence Alison as primary supervisor);
- Darren Cook (MRes (i.e., future PhD) student, co-supervised with Laurence Alison as primary supervisor);
- Some undergraduate and MSc project students working on various topics of interest.
I remain an Honorary Research Fellow in the Communications and Signal Processing Group in the Electrical and Electronic Engineering Department at Imperial College. Up until the end of 2012, I had been the "Technical Manager" for C2IS (Command and Control Information Systems) and a Senior QinetiQ fellow at QinetiQ and a Visiting Industrial Professor in the Engineering Department at Bristol University. At QinetiQ, I led projects conducting research and development (eg into different aspects of the multi-sensor multi-target tracking problem); the algorithms tackle problems such as detection, tracking, optimisation, pattern recognition, information management and intelligence processing.
In 2000, I was lucky enough to be awarded a Royal Commission for the Exhibition of 1851 Industrial Fellowship, which funded my PhD at the Signal Processing Group of Cambridge University Engineering Department. I was supervised by Professor Bill Fitzgerald at Cambridge and by Dr Neil Gordon (who is now at DSTO) and later Dr Alan Marrs at QinetiQ. My thesis was on "Sequentially Structured Bayesian Solutions". I researched how Bayesian tracking algorithms exploit the structure of problem that they tackle: time is ordered and tracking algorithms exploit the fact that knowledge of what's happening now can therefore be sufficient in terms of the past's ability to predict the future. I am now particularly interested in the ability to use the structure of problems in general in the design of algorithms for their solution. As such, I am pleased to be working on difficult problems being tackled by the Artificial Intelligence community for which I hope to develop particularly efficient and robust solutions. These include: inference in graphical models with loops (eg robustly processing very noisy images); learning strategies in partially observed games (ie getting a computer to learn from experience how to fool a human); tracking of articulated objects (eg tracking people in crowds using a network of webcams).
I live very happily with my wife, Michelle, and my two sons in Allerton in Liverpool, UK; Allerton is a leafy suburb of Liverpool, which is about two hours by (fast) train from London. I used to thoroughly enjoying playing Rugby fives and occasionally go for a run or play squash or football, but I've recently started playing tennis more. I don't sail though - that's another Simon Maskell. Things I like include: Lobster, Mange Tout, Chocolate, Pink Floyd, Goldie Lookin Chain, The Egg, Fight Club, Fifth Element, City of Lost Children, Cezanne, Matisse and Picasso. Things I don't like so much include: pickled beetroot, Justin Timberlake, Citizen Kane and Turner.
I went to South America once and took a load of pictures of the Iguazu falls which I merged together. I also went to Marloes Sands in West Wales and Kennedy Space Centre in Florida and did the same. These results look like this:
The following is planned to be an up-to-date list of my publications - time will tell. The publications document my thoughts at various points. Co-authors (who have a mention because they have websites) include Yaakov Bar-Shalom, Mark Briers (who also received one of the aforementioned Royal Commission for the Exhibition of 1851 Industrial Fellowships, to conduct his PhD at Cambridge University with Arnaud Doucet and at QinetiQ with me), Richard Everitt, Kiruba and Ben Alun-Jones. Where possible, I've provided links to versions of the documents.Some of these necessitate appropriate subscriptions to online sources (ieeexplore etc); if the links don't work, it may be because you shouldn't have access!
Journal Papers / Book Chapters
- P Green and S Maskell. Estimating the parameters of dynamical systems from Big Data using Sequential Monte Carlo samplers. Accepted for publication in Mechanical Systems and Signal Processing. 2016. (pdf of preprint)
- J Raphael, S Maskell and E Sklar. An Intersection-centric Auction-based Traffic Signal Control Framework. In Agent-Based Modeling of Sustainable Behaviors (book). Springer, 2017.(chapter)
- R Sloane, O Osanlou, D Lewis, D Bollegala, S Maskell and M Pirmohamed. Social Media and Pharmacovigilance: A Review of the Opportunities and Challenges. British Journal of Clinical Pharmacology. 2015. (pdf)
- M Limniou, J Downes, S Maskell, M Bowden and J Marshall. Datasets capturing students’ and teachers’ views on the role of learning technology. British Journal of Educational Technology, Special Issue: Open Data in Learning Technology. Volume 46, Issue 5, pages 1081–1091, September 2015.(pdf)
- H Bhaskar, L Mihaylova and S Maskell. Articulated Human Body Parts Detection Based on Cluster Background Subtraction and Foreground Matching. Neurocomputing, Special Issue on Behaviours in Video. Volume 100. Pages 58-73. 2013.(pdf)
- A Gning, L Mihaylova, S Maskell, S Pang and S Godsill. Group Object Structure and State Estimation with Evolving Networks and Monte Carlo Methods. Volume 59, Issue 4, April 2011. pp 1383-1396. IEEE Transactions on Signal Processing.(pdf)
- P Minvielle, A Doucet, A Marrs and S Maskell.A Bayesian Approach to Joint Tracking and Identification of Geometric Shapes in Video Sequences. Journal of Image and Vision Computing. Volume 28, Issue 1, January 2010, Pages 111-123.(pdf)
- M Briers, A Doucet, and S Maskell. Smoothing algorithms for state-space models. Volume 62, Number 1, 61-89. Annals of the Institute of Statistical Mathematics. December 2010.(pdf)
- S Maskell. Statistical Methods for Target Tracking. In Wiley Encyclopedia of Computer Science and Engineering (book). Edited by B W Wah. Published by Hoboken, NJ, January 2009. Vol 5. pp 2820-2829.(pdf)
- S Maskell. A Bayesian Approach to Fusing Uncertain, Imprecise and Conflicting Information. Information Fusion Journal. 9(2):259-277. April 2008.(pdf)
- S Maskell, R Everitt, R Wright and M Briers. Multi-Target Out-of-Sequence Data Association: Tracking Using Graphical Models. Information Fusion Journal, 7(4):434-447. December 2006.(pdf)
- K Hermiston and S Maskell. Fusion Challenges in the Detection and Identification of Difficult Objects and Events. Journal of Defence Science. 10(3), September 2005.
- M Rutten, N Gordon and S Maskell. Recursive Track-Before-Detect with Target Amplitude Fluctuations. IEE Proceedings on Radar Sonar Navigation, 152(5), October 2005, pp345-352 (pdf).
- S Maskell, M Briers, R Wright and P Horridge. Tracking using a Radar and a Problem Specific Proposal Distribution in a Particle Filter. IEE Proceedings on Radar Sonar Navigation, 152(5), October 2005, pp315-322 (pdf).
- S Maskell. Joint Tracking Manoevring Targets and Classification of Their Maneovrability. EURASIP JASP 2004:15 (2004) 2339-2350 (Special Issue of EURASIP Journal on Applied Signal Processing on Particle Filtering in Signal Processing) (pdf).
- S Maskell. Basics of the Particle Filter. In N Shephard and A Harvey, editors, State Space and Unobserved Component Models (book). Cambridge University Press, 2004.
- S Maskell, N Gordon, M Rollason, and D Salmond. Efficient Multitarget Tracking using Particle Filters. Journal Image and Vision Computing, 21(10):931-939, September 2003. (pdf)
- M S Arulampalam, S Maskell, N Gordon, and T Clapp. A Tutorial on Particle Filters for On-line Nonlinear/Non-Gaussian Bayesian Tracking. IEEE Transactions on Signal Processing, 50(2):174-188, February 2002. (pdf)
- Q M Nunes, B Lane, W Huang, K Altaf, L Rainbow, J Armstrong, W Greenhalf, D Fernig, C Hertz-Fowler, A Cossins, F Falciani, S Maskell, A Morris, R Sutton. Accurate Admission Transcriptomic Signature of the Severity of Acute Pancreatitis. Presented at 7th Meeting of the American Pancreatic Association, October 2016. (abstract)
- P Green and S Maskell.Parameter estimation from Big Data using a sequential Monte Carlo sampler. Proceedings of ISMA International Conference on Noise and Vibration Engineering. 2016.(pdf)
- J Raphael, S Maskell and E Sklar. An Empirical Investigation of Adaptive Traffic Control Parameters. Proceedings of the Workshop on Agents in Traffic and Transportation (ATT) at IJCAI 2016. 2016.(pdf)
- R Anderson, N Hare and S Maskell. Using a Bayesian Model for Confidence to Make Decisions That Consider Epistemic Regret. Proc Fusion 2016.(pdf)
- M Mehta, E Griffith, S Maskell and J Ralph. Geometric Separation of Superimposed Images. Proc Fusion 2016.(pdf)
R Young, S Maskell and S Parsons. Towards Tasking Sensors in a Way that Adapts to Online Learning of when Sensors Adhere to
their Performance Specifications. Proc Maths In Defence 2015.
J Raphael, S Maskell and E Sklar. From Goods to Traffic: First Steps Toward an Auction-based Traffic Signal Controller. Proc PAAMS 2015.(pdf)
C Liu, Y Zhou, F de Melo and S Maskell. Probabilistic Graphical Detector Fusion for Localization of Faces and Facial Parts. Proc SDF 2014.(pdf)
- R Lane, M Briers, T Cooper and S Maskell. Efficient Data Structures for Large Scale Tracking. Proc Fusion 2014.(pdf)
- F de Melo and S Maskell. Hybrid Gauss-Hermite filter. Proc IET Data Fusion and Target Tracking Conference. 2014.(pdf)
- S Maskell and S Julier. Optimised Proposals for Improved Propagation of Multi-modal Distributions in Particle Filters. In Proc Fusion 2013.(pdf)
- A Kountouriotis and S Maskell. Maneuvering Target Tracking Using an Unbiased Nearly Constant Heading Model. In Proc Fusion 2012. (pdf)
- P Kent, S Maskell, O Payne, S Richardson and L Scarff. Robust Background Subtraction for Automated Detection and Tracking of Targets in Wide Area Motion Imagery. Proc SPIE Conference on Optics and Photonics for Counterterrorism, Crime Fighting and Defence, 2012. (pdf)
- S Maskell. An Application of Sequential Monte Carlo Samplers: an Alternative to Particle Filters for Non-linear Non-Gaussian Sequential Inference with Zero Process Noise. Proc IET Data Fusion and Target Tracking Conference, 2012.(pdf).(IET.TV)
- P Horridge and S Maskell. Using a Probabilistic Hypothesis Density Filter to Confirm Tracks in a Multi-target Environment. In Proc SDF 2011.(pdf)
- D Nevell, S Maskell, P Horridge and H Barnett. Fusion of Data Sources with Different Levels of Trust. Proc Fusion 2010.(pdf)
- S Maskell, P Horridge, T Cooper and M Celand. Exchanging Multi-level Maps with Transformations to Support Multi-Modal Alignment. Proc SEAS DTC Conf 2010.(pdf)
- S Maskell, P Horridge, D Nevell and T Cooper. Fast Multi-Level Maps and Modelling of Complex Transformations. Proc SEAS DTC Conf 2009.(pdf)
- G Price, V Calloway, S Maskell and K Morgan. Tools and Datapaths to Support Implementation of Divide-and-Conquer Algorithms within an FPGA Vector Co-processor Methodology. Proc EMRS DTC Conf 2009.(pdf)
- P Horridge and S Maskell. A scalable method of tracking targets with dependent distributions. Proc Fusion 2009.(pdf)
- P Horridge and S Maskell. Searching for, initiating and Tracking Multiple Targets Using Existence Probabilities. Proc Fusion 2009.(pdf)
- T Cooper and S Maskell. Exchanging Uncertain Multi-Level Maps. In Proc SEAS DTC conference. 2008.(pdf)
- K Morgan and S Maskell. An FPGA Vector Co-Processing Core for Rapid Algorithm Development.In Proc EMRS DTC conference. 2008.(pdf)
- A Gning, L Mihaylova, S Maskell, S K Pang, S Godsill.Ground Target Group Structure and State Estimation with Particle Filtering. Proc. 11th International Conf. on Information Fusion, 2008.(pdf)
- A Gning, L Mihaylova, S Maskell, S K Pang, S Godsill. Evolving Networks for Group Object Motion Estimation. Proc. of the Institution of Engineering and Technology (IET) Seminar on Target Tracking and Data Fusion: Algorithms and Applications, April 2008.(pdf)
- H Bhaskar, L Mihaylova, S Maskell. Population-based Particle Filtering. Proc. of the Institution of Engineering and Technology (IET) Seminar on Target Tracking and Data Fusion: Algorithms and Applications, April 2008.(pdf).(IET.TV)
- P Horridge, S Maskell. Tracking with Inter-visibility Variables. Proc. of the Institution of Engineering and Technology (IET) Seminar on Target Tracking and Data Fusion: Algorithms and Applications, April 2008.(pdf).(IET.TV)
- J Hill, S Maskell and M Cole. Using Ship Tracking Methods to Assist in Quality Controlling and Bias Adjusting Meteorological Observations in a Marine Environment. Proc. of the Institution of Engineering and Technology (IET) Seminar on Target Tracking and Data Fusion: Algorithms and Applications, April 2008.(pdf)
- H Bhaskar, L Mihaylova, S Maskell. Human Body Parts Tracking Using Pictorial Structures and a Genetic Algorithm. Proc. of the IEEE International Conf. on Intelligent Systems, 6-8 Sept. 2008.(pdf)
- H Bhaskar, L Mihaylova, S Maskell. Multiple Body Part Tracking Using a Probabilistic Data Association Filter. NATO Symposium on "Sensors and Technology for Defence Against Terrorism", 22-25 April, 2008.(pdf)
- J Hill, S Maskell, M Cole. Using Ship Tracks to Bias Adjust the Marine Air Temperature Record. Royal Meteorological Society Conference, 2007.(pdf)
- H Bhaskar, L Mihaylova, S Maskell. Automatic Target Detection Based on Background Modeling Using Adaptive Cluster Density Estimation. 3rd German Workshop on Sensor Data Fusion: Trends, Solutions, Applications 2007.(pdf)
- S Maskell, B Alun-Jones and M Macleod. A Single Instruction Multiple Data Particle Filter. In Proceedings of Nonlinear Statistical Signal Processing Workshop 2006.(pdf)
- M Strens, J Baxter, M Hernandez, G Moon, S Kapetanakis and S Maskell. Autonomous Decision-Making for Sensor Allocation and Management. Moving Autonomy Forward Conference 2006.
- M Klaas, M Briers, N de Freitas, A Doucet, S Maskell and D Lang. Fast Particle Smoothing: If I Had a Million Particles. ICML 2006. (pdf)
- P Horridge and S Maskell. Real-Time Tracking Of Hundreds Of Targets With Efficient Exact JPDAF Implementation. Proceedings of Fusion 2006.(pdf)
- G Powell, D Marshall, P Smets, B Ristic, S Maskell. Joint Tracking and Classification of Airbourne Objects using Particle Filters and the Continuous Transferable Belief Model. Proceedings of Fusion 2006.(pdf)
- S Maskell, K Weekes and M Briers. Distributed Tracking of Stealthy Targets Using Particle Filters. 2006 IEE Seminar on Target Tracking: Algorithms and Applications.(pdf)
- M Briers, A Doucet, and S Maskell. Fixed-lag Sequential Monte Carlo Data Association. SPIE 2006.(pdf)
- K Gilholm, S Godsill, S Maskell, and D Salmond. Poisson models for extended target and group tracking. Proc. SPIE 5913, 59130R (2005). (pdf)
- M Briers, S Maskell, S Reece, S Roberts, I Rezek, VD Dang, A Rogers, NR Jennings. Dynamic sensor coalition formation to assist the distributed tracking of targets: Application to wide-area surveillance. IEE Conference on Homeland Security, 2005. (pdf)
- J Vermaak, S Maskell, M Briers, and P Perez. Bayesian visual tracking with existence process. In proceedings of International Conference Image Processing, 2005.(pdf)
- J Vermaak, S Maskell and M Briers. A Unifying Framework for Multi-Target Tracking and Existence. In proceedings of Fusion 2005.(pdf)
- P Minvielle, A Marrs, S Maskell and A Doucet. Joint Target Tracking and Identification – Part I: Sequential Monte Carlo Model-Based Approaches. In proceedings of Fusion 2005.(pdf).
- P Minvielle, A Marrs, S Maskell and A Doucet. Joint Target Tracking and Identification – Part II: Shape video computing. In proceedings of Fusion 2005.(pdf).
- J Vermaak, S Maskell and M Briers. Online Sensor Registration. In proceedings of IEEE Aerospace Conference, 2005.(pdf)
- JMC Clark, S Maskell, R Vinter and M Yaqoob. A Comparison of the Particle and Shifted Rayleigh Filters in their Application to a Multisensor Bearings-only Problem. In proceedings of IEEE Aerospace Conference, 2005.(pdf)
- M Rutten, N Gordon, and S Maskell. Particle-based Track-Before-Detect in Rayleigh Noise. Proceedings of SPIE Conference on Signal Processing of Small Targets, 2004. (pdf)
- M Rutten, S Maskell, M Briers, and N Gordon. Multi-path Track Association for Over-the-Horizon Radar Using Lagrangian Relaxation. Proceedings of SPIE Conference on Signal Processing of Small Targets, 2004. (pdf)
- S Maskell, N Gordon, N Everett, and M Robinson. Tracking Manoeuvring Targets Using a Scale Mixture of Normals. Proceedings of SPIE Conference on Signal Processing of Small Targets, 2004. (pdf)
- S Maskell, M Briers, and R Wright. Fast Mutual Exclusion. Proceedings of SPIE Conference on Signal Processing of Small Targets, 2004. (pdf)
- M Rutten, N Gordon, and S Maskell. Efficient Particle Based Track-Before-Detect in Rayleigh Noise. Proceedings of 7th International Conference on Information Fusion, 2004.(pdf)
- S Maskell, R Everitt, R Wright, and M Briers. Multi-target Out-of-Sequence Data Association. Proceedings of 7th International Conference on Information Fusion, 2004 (pdf).
- S Maskell, M Briers, and R Wright. Tracking Using a Radar and a Problem Specific Proposal Distribution in a Particle Filter. Proceedings of IEE Tracking Conference: Algorithms and Applications, 2004.(pdf)
- M Briers, S Maskell, and R Wright. A Rao-Blackwellised Unscented Kalman Filter. In Proceedings of 6th International Conference on Information Fusion, 2003. (pdf)
- M Briers, S Maskell, and M Philpott. Two-dimensional Assignment with Merged Measurements using Lagrangian Relaxation. Proceedings of SPIE Conference on Signal Processing of Small Targets, pages 283-292, 2003.(pdf)
- R Wright, S Maskell, M Briers, S Lycett. Robust Tracking of Stealthy Targets and Multi-Sensor Fusion. RAES Classified conference on Data Fusion, 2002.
- A Marrs, S Maskell, and Y Bar-Shalom. Expected Likelihood for Tracking in Clutter with Particle Filters. In O Drummond, editor, Proceedings of SPIE Conference on Signal Processing of Small Targets, pages 230-239, 2002.(pdf)
- S Maskell, N Gordon, M Rollason, and D Salmond. Efficient particle filtering for multiple target tracking with application to tracking in structured images. Proceedings of SPIE Conference on Signal Processing of Small Targets, pages 251-262, 2002.(pdf)
- X Lin, T Kirubarajan, Y Bar-Shalom, S Maskell. Comparison of EKF, Pseudo-measurement Filter and Particle Filter for a Bearings Only Tracking Problem. In Procedings of SPIE: Signal and Data Processing of Small Targets, 2002.(pdf)
- N Gordon, S Maskell, and T Kirubarajan. Efficient Particle Filters for Joint Tracking and Classification. In Procedings of SPIE: Signal and Data Processing of Small Targets, pages 439-449, 2002. (pdf)
- M Hernandez, A Marrs, N Gordon, S Maskell, and C Reed. Cramer-Rao Bounds for Nonlinear Filtering with Measurement Origin Uncertainty. Proceedings of 5th International Conference on Information Fusion, 2002. (pdf)
- M Hernandez, A Marrs, S Maskell, and M Orton. Tracking and Fusion for Wireless Sensor Networks. Proceedings of 5th International Conference on Information Fusion, 2002. (pdf)
- M Mallick, S Maskell, T Kirubarajan, N Gordon. Littoral Tracking using Particle Filter. In Proceedings of Fusion 2002. (pdf)
- S Maskell and N Gordon. A Tutorial on Particle Filters for On-line Nonlinear/Non-Gaussian Bayesian Tracking. In Proceedings of IEE Colloquium on Tracking, 2002 (pdf).
Media and Public Engagement
I've written a few articles and given a few talks for the general public recently:
I'm also helped to organise a public engagement event on "Big Data or Big Brother?"(link) as well as one on "Is AI a Threat to Mankind"(video).
- The Big Data Revolution. P4-5 of Realise Magazine. 2014.(magazine)
- Big Data in Healthcare. An executive briefing as part of the "Liverpool Big Data Collaborative for Health". 10 March 2014. (youtube)
- How Statistics can Help in the Mission to Find MH370. The Conversation. 27 March 2014.(link) This is a journalistically edited version of a University of Liverpool News Article.(link)
- Viewpoint: Big Data and the Budget. University of Liverpool News Article. 20 March 2014.(link)
- The Future of Cyber Security is in the Mind. University of Liverpool News Article. 3 March 2014.(link)
- How Can We Exploit the Opportunities of ‘Big Data’ whilst Safeguarding the Interests of Citizens? 2013.(pdf)
- I was interviewed for People in Science, Technology, Engineering & Mathematics. The video is on display at the National Space Centre's multi-touch Table.(transcript)
Patent / Thesis / Freely Available (ie not internal to QinetiQ) Technical Reports / Other
- Particle Filters—Learning from the Past, Tracking the Present and Predicting the Future. School of ICASSP presentation, 2015.(video)
- The Ubiquitous Utility of the General Linear Model and Monte-Carlo Methods. Talk at Bill Fitzgerald's Memorial Day, 2015. (video)
- Associate editor for IEEE Transacations of Aerospace and Electronic Systems as well as for IEEE Signal Processing Letters.
- S Maskell, F de Melo and F Daum. MOP: Particles without Resampling. Invited Talk at Isaac Newton Institute event on Monte Carlo Inference for Complex Statistical Models. 15 May 2014. (video)
- Invited Discussant: C Andrieu, A Doucet, and R Holenstein. Particle Markov chain Monte Carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology). Volume 72, Number 3, pp 269-342, 2010.(pdf)
- General chair for International Conference on Information Fusion 2010, Fusion 2010 (conference website), where I organised a plenary session with John Lavery (from the US Army Research Organisation, ARO): the uncertainty forum.(IET.TV)
- M Briers, A Doucet, and S Maskell. Smoothing Algorithms for State-Space Models. Cambridge University Engineering Department Technical Report, CUED/F-INFENG/TR.498, August 2004. (pdf)
- S Maskell. Signal Processing with Reduced Combinatorial Complexity. July 2003. Patent Reference:0315349.1. A free evaluation licence and associated MATLAB can be obtained by emailing: < ehm signal qinetiq com>.(pdf)
- S Maskell. Sequentially Structured Bayesian Solutions. PhD thesis, Cambridge University Engineering Department, 2004. Chapters can be briefly described as:
- A review of the tracking literature with a few new extensions.
- An algorithm for the difficult tracking problem of joint tracking and classification of targets using semi-Markov models.
- An approach for deriving the models needed for tracking algorithms from SDEs.
- An efficient (patented) method for exploiting an imposed ordering of multiple targets to improve the efficiency of algorithms such as the JPDAF.
- A technique for exploiting tracking algorithms in inference in markov meshes and so the analysis of images.
- S Maskell, M Orton, and N Gordon. Efficient Inference for Conditionally Gaussian Markov Random Fields. Technical report, Cambridge University Engineering Department, 2002.
- S Maskell. Multi-Sensor Management. First Year PhD Report. Technical Report, Cambridge University Engineering Department, June 2001.