Skip to main content Accessibility help
×
Hostname: page-component-5c6d5d7d68-pkt8n Total loading time: 0 Render date: 2024-08-09T06:13:27.704Z Has data issue: false hasContentIssue false

11 - Approximate likelihood estimation of static parameters in multi-target models

from IV - Multi-object models

Published online by Cambridge University Press:  07 September 2011

Sumeetpal S. Singh
Affiliation:
University of Cambridge
Nick Whiteley
Affiliation:
University of Bristol
Simon J. Godsill
Affiliation:
University of Cambridge
David Barber
Affiliation:
University College London
A. Taylan Cemgil
Affiliation:
Boğaziçi Üniversitesi, Istanbul
Silvia Chiappa
Affiliation:
University of Cambridge
Get access

Summary

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

[1] Y., Bar-Shalom and T. E., Fortmann. Tracking and Data Association. Mathematics in science and engineering. Academic Press, 1964.Google Scholar
[2] S., Blackman and R., Popoli, editors. Design and Analysis of Modern Tracking Systems. Artech House radar library. Artech House, 1999.Google Scholar
[3] O., Cappé, S. J., Godsill and E., Moulines. An overview of existing methods and recent advances in sequential Monte Carlo. Proceedings of the IEEE, 96(5):899–924, 2007.Google Scholar
[4] D., Clark, A. T., Cemgil, P., Peeling and S. J., Godsill. Multi-object tracking of sinusoidal components in audio with the Gaussian mixture probability hypothesis density filter. In Proc. of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2007.Google Scholar
[5] D. E., Clark and J., Bell. Convergence results for the particle PHD filter. IEEE Transactions on Signal Processing, 54(7):2652–2661, 2006.Google Scholar
[6] P., Del Moral, A., Doucet and A., Jasra. Sequential Monte Carlo methods for Bayesian computation. In Bayesian Statistics 8. Oxford University Press, 2006.Google Scholar
[7] A., Doucet, N., de Freitas and N., Gordon, editors. Sequential Monte Carlo Methods in Practice. Statistics for Engineering and Information Science. Springer Verlag, 2001.Google Scholar
[8] A. M., Johansen, S., Singh, A., Doucet and B., Vo. Convergence of the SMC implementation of the PHD filter. Methodology and Computing in Applied Probability, 8(2):265–291, 2006.Google Scholar
[9] J., Kiefer and J., Wolfowitz. Stochastic estimation of the maximum of a regression function. Annals of Mathematical Statistics, 23(3):462–466, 1952.Google Scholar
[10] J. F. C., Kingman. Poisson Processes. Oxford Studies in Probability. Oxford University Press, 1993.Google Scholar
[11] N. L., Kleinman, J. C., Spall and D. Q., Naiman. Simulation–based optimisation with stochastic approximation using common random numbers. Management Science, 45(11):1571–1578, 1999.Google Scholar
[12] J., Lund and E., Thonnes. Perfect simulation and inference for point processes given noisy observations. Computational Statistics, 19(2):317–336, 2004.Google Scholar
[13] R., Mahler. Statistical Multisource-Multitarget Information Fusion. Artech House, 2007.Google Scholar
[14] R. P. S., Mahler. Multitarget Bayes filtering via first-order multitarget moments. IEEE Transactions on Aerospace and Electronic Systems, pages 1152–1178, 2003.Google Scholar
[15] J., Mullane, B., Vo, M. D., Adams and W. S., Wijesoma. A PHD filtering approach to robotic mapping. In IEEE Conf. on Control, Automation, Robotics and Vision, 2008.Google Scholar
[16] G., Pflug. Optimization of Stochastic Models: The Interface between Simulation and Optimization. Kluwer Academic Publishers, 1996.Google Scholar
[17] G., Poyiadjis, A., Doucet and S. S., Singh. Maximum likelihood parameter estimation using particle methods. In Proceedings of the Joint Statistical Meeting, 2005.Google Scholar
[18] G., Poyiadjis, A., Doucet and S. S., Singh. Monte Carlo for computing the score and observed information matrix in state-space models with applications to parameter estimation. Technical Report CUED/F-INFENG/TR.628, Signal Processing Laboratory, Department of Engineering, University of Cambridge, 2009.
[19] D., Reid. An algorithm for tracking multiple targets. IEEE Transactions on Automatic Control, 24:843854, 1979.Google Scholar
[20] H., Siddenblath. Multi-target particle filtering for the probability hypothesis density. In Proceedings of the International Conference on Information Fusion, Cairns, Australia, pages 800–806, 2003.Google Scholar
[21] S. S., Singh, B.-N., Vo, A., Baddeley and S., Zuyev. Filters for spatial point processes. SIAM Journal on Control and Optimization, 48:2275–2295, 2009.Google Scholar
[22] J. C., Spall. Multivariate stochastic approximation using simultaneous perturbation gradient approximation. IEEE Transations on Automatic Control, 37(3):332–341, 1992.Google Scholar
[23] J. C., Spall. Introduction to Stochastic Search and Optimization. Wiley-Interscience, 1st edition, 2003.Google Scholar
[24] C. B., Storlie, C. M., Lee, J., Hannig and D., Nychka. Tracking of multiple merging and splitting targets: A statistical perspective (with discussion). Statistica Sinica, 19(1):152, 2009.Google Scholar
[25] B., Vo and W.-K., Ma. The Gaussian mixture probability hypothesis density filter. IEEE Trans. Signal Processing, 54(11):4091–4104, 2006.Google Scholar
[26] B., Vo, S., Singh and A., Doucet. Random finite sets and sequential Monte Carlo methods in multi-target tracking. In Proceedings of the International Conference on Information Fusion, Cairns, Australia, pages 792–799, 2003.Google Scholar
[27] B., Vo, S., Singh and A., Doucet. Sequential Monte Carlo methods for multitarget filtering with random finite sets. IEEE Transactions on Aerospace and Electronic Systems, 41(4):1224–1245, 2005.Google Scholar
[28] N., Whiteley, S., Singh and S., Godsill. Auxiliary particle implementation of the probability hypothesis density filter. IEEE Transactions on Aerospace and Electronic Systems, 43(3):1437–1454, 2010.Google Scholar
[29] J. W., Yoon and S. S., Singh. A Bayesian approach to tracking in single molecule fluorescence microscopy. Technical Report CUED/F-INFENG/TR-612, University of Cambridge, September 2008. Working paper.
[30] T., Zajic and R. P. S., Mahler. Particle-systems implementation of the PHD multitarget tracking filter. In Proceedings of SPIE, pages 291–299, 2003.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×