Simon Godsill
Background  Research  Publications  Teaching
Position: Professor of Statistical Signal Processing
Office Location: BE309
Telephone: +44 1223 332604
Email: sjg [at] eng.cam.ac.uk
Dept. of Engineering,
University of Cambridge,
Trumpington Street,
Cambridge.
CB2 1PZ, UK
Background
Simon Godsill is Professor of Statistical Signal Processing in the Engineering Department at Cambridge University. He is also a Professorial Fellow and tutor at Corpus Christi College Cambridge. He coordinates an active research group in Signal Inference and its Applications and is Head of the Signal Processing and Communications Laboratory at Cambridge, specializing in Bayesian computational methodology, multiple object tracking, spatiotemporal inference, audio and music processing, and financial time series modeling. A particular methodological theme over recent years has been the development of novel techniques for optimal Bayesian filtering and smoothing, using Sequential Monte Carlo or Particle Filtering methods.
Publications:
Symplectic publications list here.
See Google Scholar citations, where many of our papers can be found for download.
Selected Recent Publications:
Journals:
F. Septier A. Gning S. K. Pang S. Godsill L. Mihaylova, A. Carmi. Overview of sequential Bayesian Monte Carlo methods for group and extended object tracking. Digital Signal Processing, 25, February 2014.
Pete Bunch and Simon Godsill. Approximations of the optimal importance density using Gaussian particle flow importance sampling. Journal of the American Statistical Association, 2015.
Tatjana Lemke, Marina Riabiz, and Simon J Godsill. Fully Bayesian inference for αstable distributions using a Poisson series representation. Digital Signal Processing , 47:96–115, 2015.
Bashar I Ahmad, James K Murphy, Patrick M Langdon, Simon J Godsill, Robert Hardy, and Lee Skrypchuk. Intent inference for hand pointing gesture based interactions in vehicles. IEEE transactions on cybernetics, 46(4):878– 889, 2016.
F. Ahmad, J. Murphy, D. Vatansever, E. Stamatakis, and S. Godsill. Bayesian inference of taskbased functional brain connectivity using Markov chain Monte Carlo methods. Journal of Selected Topics in Signal Processing, 2016. (Accepted).
P. Bunch, J. Murphy, and S. Godsill. Bayesian learning of degenerate linear Gaussian state space models using Markov chain Monte Carlo. IEEE Trans actions on Signal Processing, 64(16), 2016.
Fredrik Lindsten, Pete Bunch, Simo S¨arkk¨a, Thomas B Sch¨on, and Simon J Godsill. Raoblackwellized particle smoothers for conditionally linear gaussian models. IEEE Journal of Selected Topics in Signal Processing, 10(2):353–365, 2016.
James Murphy and Simon J Godsill. Blocked particle Gibbs schemes for high dimensional interacting systems. IEEE Journal of Selected Topics in Signal Processing, 10(2):328–342, 2016.
Geliang Zhang, Hugh Christensen, Guolong Li, and Simon Godsill. A correction note for price dynamics in a Markovian limit order market. SIAM Journal on Financial Mathematics, 7(1):152–158, 2016.
Conferences:
Bashar I Ahmad, Patrick M Langdon, Pete Bunch, and Simon J Godsill. Prob abilistic intentionality prediction for target selection based on partial cursor tracks. In International Conference on Universal Access in HumanComputer Interaction, pages 427–438. Springer International Publishing, 2014.
Bashar I Ahmad, Patrick M Langdon, Simon J Godsill, Robert Hardy, Eduardo Dias, and Lee Skrypchuk. Interactive displays in vehicles: Improving usability with a pointing gesture tracker and bayesian intent predictors. In Proceed ings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pages 1–8. ACM, 2014.
Bashar I Ahmad, James Murphy, Patrick M Langdon, and Simon J Godsill. Bayesian target prediction from partial finger tracks: Aiding interactive dis plays in vehicles. In Information Fusion (FUSION), 2014 17th International Conference on, pages 1–7. IEEE, 2014.
Bashar I Ahmad, James Murphy, Patrick M Langdon, and Simon J Godsill. Filtering perturbed invehicle pointing gesture trajectories: Improving the reliability of intent inference. In 2014 IEEE International Workshop on Ma chine Learning for Signal Processing (MLSP), pages 1–6. IEEE, 2014.
B.I. Ahmad, J. Murphy, P.M. Langdon, and S.J. Godsill. Bayesian target pre diction from partial finger tracks: Aiding interactive displays in vehicles. InInformation Fusion (FUSION), 2014 17th International Conference on, pages 1–7, July 2014.
B.I. Ahmad, J. Murphy, P.M. Langdon, and S.J. Godsill. Filtering perturbed invehicle pointing gesture trajectories: Improving the reliability of intent inference. InMachine Learning for Signal Processing (MLSP), 2014 IEEE International Workshop on, pages 1–6, Sept 2014.
T. Lemke and S.J. Godsill. A poisson series approach to Bayesian Monte Carlo in ference for skewed alphastable distributions. In Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on, pages 8023– 8027, May 2014.
M.R. Mestre, S.J. Godsill, and W.J. Fitzgerald. Bayesian detection of single trial eventrelated potentials. In Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on, pages 4693–4697, May 2014.
James Murphy and Simon Godsill. Roadassisted multiple target tracking in clutter. In Information Fusion (FUSION), 2014 17th International Confer ence on , pages 1–8. IEEE, 2014.
Bashar I Ahmad, James Murphy, Patrick M Langdon, Robert Hardy, and Si mon J Godsill. Destination inference using bridging distributions. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages 5585–5589. IEEE, 2015.
Simon Godsill, Herbert Buchner, and Jan Skoglund. Detection and suppression of keyboard transient noise in audio streams with auxiliary keybed micro phone. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 379–383. IEEE, 2015.
James Murphy and Simon Godsill. Bayesian parameter estimation of jump Langevin systems for trend following in finance. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 4125–4129. IEEE, 2015.
James Murphy and Simon Godsill. Efficient filtering and sampling for a class of timevarying linear systems. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 3701–3705. IEEE, 2015.
Research Areas  Signal Inference and its Applications.
 Bayesian Computational Methods (including Particle Filters/Smoothers and MCMC
 Audio and Music Processing
 Tracking and Spatiotemporal Inference
Bayesian Computational Methods for Signal Processing
Underpinning much of our work is the Bayesian paradigm and associated algorithms for inference about the parameters and structure of complex systems. In the Bayesian approach data is combined with any prior information available in an optimal fashion using probability distributions. We are particularly concerned with the development of new methods appropriate to the applications above. These applications are often sequential in nature (the data arrive onebyone and a decision/estimate is required with small or no delay), hence we focus considerable attention on sequential learning methods such as Sequential Monte Carlo (particle filtering). Other problems are batch in nature (the data arrive all at once, or we can wait until all of the data have arrived before processing)  in those cases batch algorithms can be used, and we focus attention on stochastic simulation methods such as Markov chain Monte Carlo (MCMC), including those for model uncertainty problems (reversible jump MCMC, etc.). Novel techniques are developed to help tailor these methods to the applications at hand.
Selected papers:
Recent:
F. Septier A. Gning S. K. Pang S. Godsill L. Mihaylova, A. Carmi. Overview of sequential Bayesian Monte Carlo methods for group and extended object tracking. Digital Signal Processing, 25, February 2014.
Pete Bunch and Simon Godsill. Approximations of the optimal importance density using gaussian particle flow importance sampling. Journal of the American Statistical Association, 0(ja):0–0, 2015.
Xi Chen, Simo Sarkka, and Simon Godsill. A Bayesian particle filtering method for brain source localisation. Digital Signal Processing, 47:192 – 204, 2015. Special Issue in Honour of William J. (Bill) Fitzgerald.
Tatjana Lemke, Marina Riabiz, and Simon J Godsill. Fully Bayesian inference for αstable distributions using a Poisson series representation. Digital Signal Processing, 47:96–115, 2015.
P. Bunch, J. Murphy, and S. Godsill. Bayesian learning of degenerate linear Gaussian state space models using Markov chain Monte Carlo. IEEE Trans actions on Signal Processing , 64(16), 2016.
Fredrik Lindsten, Pete Bunch, Simo S¨arkk¨a, Thomas B Sch¨on, and Simon J Godsill. Raoblackwellized particle smoothers for conditionally linear gaussian models. IEEE Journal of Selected Topics in Signal Processing, 10(2):353–365, 2016.
James Murphy and Simon J Godsill. Blocked particle gibbs schemes for high dimensional interacting systems. IEEE Journal of Selected Topics in Signal Processing, 10(2):328–342, 2016.
Pre2012 archive:
Markov Chain Monte Carlo (MCMC) methods, including model uncertainty:
 Lemke, T and Godsill, SJ (2012) Linear Gaussian computations for nearexact Bayesian Monte Carlo inference in skewed alphastable time series models. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing  Proceedings. pp. 37373740. ISSN 15206149
 Lemke, T and Godsill, SJ (2011) Enhanced poisson sum representation for alphastable processes. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing  Proceedings. pp. 41004103. ISSN 1520614
 P. J. Wolfe, S. J. Godsill, and W.J. Ng. Bayesian variable selection and regularisation for timefrequency surface estimation . Journal of the Royal Statistical Society, Series B, 2004.
 J. Vermaak, C. Andrieu, A. Doucet, and S. J. Godsill. Bayesian model selection of autoregressive processes. J. Time Series Anal. (In Press).
 S. J. Godsill. Discussion of `transdimensional Markov chain Monte Carlo' by Peter J. Green. In Highly Structured Stochastic Systems. OUP, 2003.
 Doucet, S. J. Godsill, and C. P. Robert. Marginal maximum a posteriori estimation using MCMC. Statistics and Computing, 12:7784, 2002.
 S. J. Godsill. On the relationship between Markov chain Monte Carlo methods for model uncertainty. J. Comp. Graph. Stats., 10(2):230248, 2001.
 Paul T. Troughton and Simon J. Godsill. MCMC methods for restoration of nonlinearly distorted autoregressive signals. Signal Processing, 81(1):8397, 2001.
 S. J. Godsill. Inference in symmetric alphastable noise using MCMC and the slice sampler. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, volume VI, pages 38063809, 2000.
 S. J. Godsill. MCMC and EMbased methods for inference in heavytailed processes with alphastable innovations. In Proc. IEEE Signal processing workshop on higherorder statistics, June 1999. Caesarea, Israel.
 S. J. Godsill and E. E. Kuruoglu. Bayesian inference for time series with heavytailed symmetric alpha stable noise processes. CUED Tech rep INFENG...In Proc. Applications of heavy tailed distributions in economics, engineering and statistics, June 1999. Washington DC, USA.
Sequential Monte Carlo (particle filtering and smoothing) methods (pre2013):
 Bunch, P and Godsill, SJ (2013) Particle Smoothing Algorithms for Variable Rate Models. IEEE Transactions on Signal Processing, 61. pp. 16631675.
 Christensen, HL and Murphy, J and Godsill, SJ (2012) Forecasting highfrequency futures returns using online langevin dynamics. IEEE Journal on Selected Topics in Signal Processing, 6. pp. 366380. ISSN 19324553
 Carmi, A and Septier, F and Godsill, SJ (2012) The Gaussian mixture MCMC particle algorithm for dynamic cluster tracking. Automatica, 48. pp. 24542467. ISSN 00051098
 Särkkä, S and Bunch, P and Godsill, SJ (2012) A backwardsimulation based RaoBlackwellized particle smoother for conditionally linear Gaussian models. IFAC Proceedings Volumes (IFACPapersOnline), 16. pp. 506511. ISSN 14746670
 Pang, SK and Li, J and Godsill, SJ (2011) Detection and Tracking of Coordinated Groups. IEEE T AERO ELEC SYS, 47. pp. 472502. ISSN 00189251
 de Villiers, JP and Godsill, SJ and Singh, SS (2011) Particle predictive control. Journal of Statistical Planning and Inference, 141. pp. 17531763. ISSN 03783758
 Whiteley, NP and Singh, SS and Godsill, SJ (2010) Auxiliary particle implementation of the probability hypothesis density filter. IEEE Transactions on Aerospace and Electronic Systems, 46. pp. 14371454. ISSN 00189251
 Whiteley, NP and Johansen, AM and Godsill, SJ (2010) Monte Carlo filtering of piecewise deterministic processes. Journal of Computational and Graphical Statistics. pp. 121. ISSN 10618600
 O. Cappé, S.J. Godsill, and E.Moulines. An overview of existing methods and recent advances in sequential monte carlo. Proc. IEEE, May 2007.
 S.J. Godsill, J. Vermaak, KF. Ng, and JF. Li. Models and algorithms for tracking of manoeuvring objects using variable rate particle filters. Proc. IEEE, May 2007.
 Lombardi, MJ and Godsill, SJ (2006) Online Bayesian estimation of signals in symmetric /spl alpha/stable noise. IEEE Transactions on Signal Processing, 54. pp. 775779. ISSN 1053587X
 S. J. Godsill and J. Vermaak, Models and algorithms for tracking using transdimensional sequential Monte Carlo. In Proc. IEEE ICASSP 2004.#
 S.J. Godsill and A. Doucet and M. West. Monte Carlo smoothing for nonlinear time series. Journal of the American Statistical Association. Vol.50, pp. 438449, 2004
 J. Vermaak, S. J. Godsill, and A. Doucet. Radial basis function regression using transdimensional sequential Monte Carlo. In IEEE Workshop on Statistical Signal Processing, 2003.
 J. Vermaak, S. J. Godsill, and A. Doucet. Sequential Bayesian kernel regression. In Advances in Neural Information Processing Systems 16, Cambridge, MA. MIT Press, 2003.
 W. Fong, S. J. Godsill, A. Doucet, and M. West. Monte Carlo smoothing with application to speech enhancement. IEEE Trans. on Signal Processing, 50(2):438449, February 2002.
 J. Vermaak, C. Andrieu, A. Doucet, and S. J. Godsill. Particle methods for Bayesian modelling and enhancement of speech signals . IEEE Trans. on Speech and Audio Processing, 10(3):173185, 2002.
 S. J. Godsill and T. C. Clapp. Improvement strategies for Monte Carlo particle filters. In A Doucet, J. F. G. De Freitas, and N. J. Gordon, editors, Sequential Monte Carlo Methods in Practice. New York: SpringerVerlag, 2001.
 S. J. Godsill, A Doucet, and M West. Maximum a posteriori sequence estimation using Monte Carlo particle filters. Ann. Inst. Stat. Math., 53(1):8296, March 2001.
 Doucet, S. J. Godsill, and C. Andrieu. On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 10:197208, 2000.
 T. C. Clapp and S. J. Godsill. Fixedlag smoothing using sequential importance sampling. In J.M. Bernardo, J.O. Berger, A.P. Dawid, and A.F.M. Smith, editors, Bayesian Statistics VI, pages 743752. Oxford University Press
Archival Tutorial Materials:
 Bayesian Computer intensive methods for Statistical Signal Processing (Plenary Address, IEEE Workshop on Statistical Signal Processing, St Louis August 2003)
 OnLine Bayesian Methods for estimation of nonlinear nonGaussian signals (Tutorial for Opening Workshop of SAMSI programme, North Carolina, Sept. 2002)
Audio and Music Processing (AMP)
The Signal Processing Laboratory has had long involvement in audio and music processing. Early work in sound restoration here in the 1980's led to the founding of the successful company CEDAR Audio Ltd. which produces DSP equipment for remastering and enhancement of sound in the recording, broadcast and forensic industries. In current research we are concerned with accurate modelling of digital audio and automated inference about the parameters and structure of those models. Research interests include computer music transcription, audio source separation, musical beattracking, chord recognition, Digital Audio Restoration, noise reduction, multichannel audio and sparse modelling with overcomplete dictionaries of atoms. Underpinning much of the work is a Bayesian statistical modelling approach to audio problems, see below.
See material for ICASSP 2015 paper on keystroke removal
Selected papers on the Audio Processing topic:

Geliang Zhang and Simon Godsill. Fundamental frequency estimation in speech signals with variable rate particle filters. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 24(5):890–900, 2016.

Fallon, MF and Godsill, SJ (2012) Acoustic Source Localization and Tracking of a TimeVarying Number of Speakers. IEEE T AUDIO SPEECH, 20. pp. 14091415. ISSN 15587916
 Nielsen, JK and Christensen, MG and Cemgil, AT and Godsill, SJ and Jensen, SH (2011) Bayesian Interpolation and Parameter Estimation in a Dynamic Sinusoidal Model. IEEE T AUDIO SPEECH, 19. pp. 19861998. ISSN 15587916
 Christensen, JEN and Godsill, SJ (2011) Bayesian classification of acoustical waveforms under environmental variability. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics. pp. 281284.
 Peeling, PH and Godsill, SJ (2011) Multiple pitch estimation using nonhomogeneous poisson processes. IEEE Journal on Selected Topics in Signal Processing, 5. pp. 11331143. ISSN 19324553
 Godsill, SJ (2010) The shifted inversegamma model for noise floor estimation in archived audio recordings. Signal Processing, 90. pp. 991999. ISSN 01651684
 Fallon, M and Godsill, SJ (2009) Acoustic source localisation and tracking using track before detect. IEEE Transactions on Audio, Speech and Language Processing, PP. p. 1. ISSN 15587916
 Peeling, PH and Cemgil, AT and Godsill, SJ (2009) Generative spectrogram factorisation models for polyphonic piano transcription. IEEE Transactions on Audio, Speech and Language Processing, 18. pp. 519527. ISSN 15587916
 Fevotte, C and Torresani, B and Daudet, L and Godsill, SJ (2007) Sparse linear regression with structured priors and application to denoising of musical audio. IEEE Transactions on Audio Speech and Language Processing, 16. pp. 174185. ISSN 15587916
 Cemgil, AT and Godsill, SJ and Fevotte, C (2007) Variational and stochastic inference for Bayesian source separation. Digital Signal Processing, 17. pp. 891913. ISSN 10512004
 Davy, M and Godsill, SJ (2006) Bayesian analysis of polyphonic western tonal music. Journal of the Acoustical Society of America, 119. pp. 24982517. ISSN 00014966
 Fevotte, C and Godsill, SJ (2006) A Bayesian approach for blind separation of sparse sources. IEEE Transactions on Audio, Speech and Language Processing, 14. pp. 21742188. ISSN 15587916
 Lombardi, MJ and Godsill, SJ (2006) Online Bayesian estimation of signals in symmetric /spl alpha/stable noise. IEEE Transactions on Signal Processing, 54. pp. 775779. ISSN 1053587X
 Fevotte, C and Godsill, SJ (2006) Sparse linear regression in unions of bases via Bayesian variable selection. IEEE Signal Processing Letters, 13. pp. 441444. ISSN 10709908
 P. J. Wolfe, S. J. Godsill, and W.J. Ng. Bayesian variable selection and regularisation for timefrequency surface estimation. Journal of the Royal Statistical Society, Series B 66(3):575589, 2004.
 M.Davy and S. J. Godsill. Bayesian harmonic models for musical signal analysis (with discussion). In J.M. Bernardo, J.O. Berger, A.P. Dawid, and A.F.M. Smith, editors, Bayesian Statistics VII. Oxford University Press, 2003.
 S. J. Godsill and P. J. W. Rayner. Robust reconstruction and analysis of autoregressive signals in impulsive noise using the Gibbs sampler IEEE Trans. on Speech and Audio Processing, 6(4):352372, July 1998.
 Godsill, SJ and Rayner, PJW (1995) Bayesian approach to the restoration of degraded audio signals. IEEE Transactions on Speech and Audio Processing, 3. pp. 267278. ISSN 10636676
Book:
 S.J. Godsill and P.J.W. Rayner. Digital Audio Restoration  a statistical modelbased approach (Berlin: SpringerVerlag 1998)
Archival Talks:
 Bayesian harmonic models for musical signal analysis (with M. Davy). Invited lecture for Seventh Valencia conference on Bayesian Statistics  Tenerife, 2002
 Digital Audio Restoration. An introductory talk given at Helsinki University of Technology, Fall 2003
 An introduction to MCMC methods for sparse overcomplete sparse audio models. Tutorial for European Union Project HASSIP workshop, Cambridge, Sept. 2006
Tracking Algorithms
A major challenge in many application areas is that of detection, classification and tracking of multiple objects. Classic applications of this include radar and sonar, but the principles extend into computer vision, robotics and many other areas. We are aiming to push back the boundaries of current technology where many objects are present, detection probabilities are low and clutter rates are high. The methods devised use novel implementations of Monte Carlo Bayesian updating to carry out joint detection of number, characteristics and position of objects in cluttered environments.
Selected papers
F. Septier A. Gning S. K. Pang S. Godsill L. Mihaylova, A. Carmi. Overview of sequential Bayesian Monte Carlo methods for group and extended object tracking. Digital Signal Processing, 25, February 2014.
Pete Bunch and Simon Godsill. Approximations of the optimal importance density using gaussian particle flow importance sampling. Journal of the American Statistical Association, 0(ja):0–0, 2015.
Xi Chen, Simo Sarkka, and Simon Godsill. A Bayesian particle filtering method for brain source localisation. Digital Signal Processing, 47:192 – 204, 2015. Special Issue in Honour of William J. (Bill) Fitzgerald.
Bashar I Ahmad, James K Murphy, Patrick M Langdon, Simon J Godsill, Robert Hardy, and Lee Skrypchuk. Intent inference for hand pointing gesture based interactions in vehicles. IEEE transactions on cybernetics , 46(4):878– 889, 2016.
Fredrik Lindsten, Pete Bunch, Simo S¨arkk¨a, Thomas B Sch¨on, and Simon J Godsill. Raoblackwellized particle smoothers for conditionally linear gaussian models. IEEE Journal of Selected Topics in Signal Processing , 10(2):353–365, 2016.
James Murphy and Simon J Godsill. Blocked particle gibbs schemes for high dimensional interacting systems. IEEE Journal of Selected Topics in Signal Processing, 10(2):328–342, 2016.
Pre2013:
 Bunch, P and Godsill, SJ (2013) Particle Smoothing Algorithms for Variable Rate Models. IEEE Transactions on Signal Processing, 61. pp. 16631675.
 Carmi, A and Septier, F and Godsill, SJ (2012) The Gaussian mixture MCMC particle algorithm for dynamic cluster tracking. Automatica, 48. pp. 24542467. ISSN 00051098
 Pang, SK and Li, J and Godsill, SJ (2011) Detection and Tracking of Coordinated Groups. IEEE T AERO ELEC SYS, 47. pp. 472502. ISSN 00189251
 de Villiers, JP and Godsill, SJ and Singh, SS (2011) Particle predictive control. Journal of Statistical Planning and Inference, 141. pp. 17531763. ISSN 03783758
 Whiteley, NP and Singh, SS and Godsill, SJ (2010) Auxiliary particle implementation of the probability hypothesis density filter. IEEE Transactions on Aerospace and Electronic Systems, 46. pp. 14371454. ISSN 00189251
 Whiteley, NP and Johansen, AM and Godsill, SJ (2010) Monte Carlo filtering of piecewise deterministic processes. Journal of Computational and Graphical Statistics. pp. 121. ISSN 10618600
 S.J. Godsill, J. Vermaak, KF. Ng, and JF. Li. Models and algorithms for tracking of manoeuvring objects using variable rate particle filters. Proc. IEEE, May 2007.
 S.J. Godsill. Particle filters for continuoustime jump models in tracking applications. In ESAIM: PROCEEDINGS of Oxford Workshop on Particle Filtering, 2007.
 J. Vermaak, N. Ikoma, and S.J. Godsill. Sequential Monte Carlo framework for extended object tracking. IEE Proc.Radar Sonar Navig., 152(5):353363, October 2005.
 J. Vermaak, S. Godsill, and P. Perez. Monte Carlo filtering for multitarget tracking and data association. IEEE Tr. Aerospace and Electronic Systems, 41(1):309332, January 2005.
 K. Gilholm, S.J. Godsill, S. Maskell, and D. Salmond. Poisson models for extended target and group tracking. In Proc. SPIE: Signal and Data Processing of Small Targets, 2005.
 S. J. Godsill and J. Vermaak. Variable rate particle filters for tracking applications. In Proc. IEEE Stat. Sig. Proc., Bordeaux, 2005.
 W. Ng, J.F. Li, S.J. Godsill, and J. Vermaak. A hybrid approach for online joint detection and tracking for multiple targets. In IEEE Aerospace Conference, 2005.
 S. J. Godsill and J. Vermaak, Models and algorithms for tracking using transdimensional sequential Monte Carlo. In Proc. IEEE ICASSP 2004
Research Projects with: DIFDTC, QinetiQ, DSO Singapore, EPSRC, ...
Genomic and Life Sciences Signal Processing
A further topic of great importance is the interpretation and analysis of genomic data  for example the sequencing of the human genome. Any improvements achievable in this area are likely to lead to improvements our understanding of genetics and in treatment for diseases such as cancer. Work to date has focused on improving the performance of DNA sequencing machines through very accurate Bayesian modelling. Currents topics of work include the accurate preprocessing of microarray data  crucial in identification of the genes active in certain diseases.
Selected papers (pre2006):
 Ji Won Yoon, Simon Godsill, Eriks Kupce, and Ray Freeman. Deterministic and statistical methods for reconstructing multidimensional nmr spectra . Magnetic Resonance in Chemistry, March 2006.
 N. M. Haan and S. J. Godsill. Bayesian models for DNA sequencing. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, 2002
 N. M. Haan and S. J. Godsill. A timevarying model for DNA sequencing data submerged in correlated noise. In Proc. IEEE Workshop on Statistical Signal Processing, August 2001
 N.M. Haan and S.J. Godsill. Sequential methods for DNA sequencing. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, 2001.
 N. M. Haan and S. J. Godsill. Modelling electropherogram data for DNA sequencing using variable dimension MCMC. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, 2000.
Teaching
 3F3 Signal and Pattern processing
 IB Paper 6  Signal and Data Analysis
Other links:
 Book  Digital Audio Restoration  a statistical modelbased approach (SpringerVerlag 1998)
 Bayesian Picture Gallery
[ Cambridge University  CUED  Signal Processing Group ]
Updated August 2016
sjg [at] eng.cam.ac.uk