Distributed tracking of stealthy targets using particle filters

IET Seminar Digest

Volumn 2006, Issue 11359, 2006, Pages 17-26

  Maskell, S R ^a   Weekes, K R ^a   Briers, M ^a  

^a QINETIQ   (United Kingdom)

Author keywords

Bayesian inference; Data fusion; Distributed tracking; Particle filters; Sequential monte carlo; Track fusion

Indexed keywords

BAYESIAN INFERENCE; DISTRIBUTED TRACKING; PARTICLE FILTERS; SEQUENTIAL MONTE CARLO; TRACK FUSION;

AIR FILTERS; BAYESIAN NETWORKS; DISTRIBUTED COMPUTER SYSTEMS; INFERENCE ENGINES; INFORMATION FUSION; MONTE CARLO METHODS; NONLINEAR FILTERING; SENSOR DATA FUSION; SENSOR NODES; TARGETS; TELECOMMUNICATION EQUIPMENT;

TARGET TRACKING;

EID: 67650463350     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1049/ic:20060553     Document Type: Conference Paper

References (20)

1. APL. The Cooperative Engagement Capability. Johns Hopkins APL Technical Digest, 16(4), 1995.
2. T Berhard and J-P Le Cadre. Distributed target tracking for nonlinear systems: Application to bearings-only tracking. In Proceedings of International Fusion Conference, 2005.
3. Y Boers and J Driessen. A particle Filter based detection scheme. IEEE Signal Processing Letters, 10(10):300{302, 2003.
4. M Briers, A Doucet, and S R Maskell. Smoothing algorithms for state-space models. Technical Report CUED/F-INFENG/TR.498, Cambridge University Engineering Department, August 2004.
5. M Briers, A Doucet, and S Singh. Sequential auxiliary particle belief propagation. In Proceedings of International Fusion Conference, 2005.
6. C Chong and S Mori. Convex combination and covariance intersection algorithms in distributed fusion. In Proceedings of Fusion, August 2001.
7. N Chopin. A sequential particle Filter method for static models. Biometrika, 89(3):539{552, 2002.
8. M Coates. Distributed particle Filters for sensor networks. In Proceedings of the third international symposium on Information processing in sensor networks, pages 99{107, 2004.
9. A Doucet, J F G de Freitas, and N J Gordon, editors. Sequential Monte Carlo Methods in Practice. Springer-Verlag: New York, 2001.
10. N Gordon, D Salmond, and A Smith. Novel approach to nonlinear/non- Gaussian Bayesian state estimation. IEE Proceedings-F, 140(2):107{113, April 1993.
11. M Klaas, J F G De Freitas, and A Doucet. Towards practical N2 Monte Carlo: The marginal particle Filter. In Proceedings of UAI, 2005.
12. P Del Moral, A Doucet, and A Jasra. Sequential Monte Carlo Samplers. To appear in Journal of the Royal Statistical Society, 2006.
13. M Paskin and C Guestrin. Robust probabilistic inference in distributed systems. In Proceedings of the Twentieth Conference on Uncertainty in ArtiFicial Intelligence, 2004.
14. S Reece and S Roberts. Bounded covariance inflation and an application in low-bandwidth decentralised slam. In Proceedings of International Fusion Conference, 2005.
15. M Rossencrantz, G Fordon, and S Thrun. Decentralised sensor fusion with distributed particle Filters. In Proceedings of UAI, 2003.
16. M Rutten, N Gordon, and S Maskell. E±cient particle-based track-before-detect in Rayleigh noise. In Proceedings of Information Fusion Conference, pages 693{700, Jun 2004.
17. D Salmond and H Birch. A particle Filter for track-before-detect. In Proceedings of the American Control Conference, June 2001.
18. S Thrun, D Koller, Z Gharamani, and H Durrant-Whyte. Simultaneous mapping and localization with sparse extended information Filters: Theory and initial results. Technical Report CMU-CS-02-112, CMU, 2002.
19. J Vermaak, S Godsill, and A Doucet. Sequential Bayesian kernel regression. In Sebastian Thrun, Lawrence Saul, and Bernhard SchÄolkopf, editors, Advances in Neural Information Processing Systems 16. MIT Press, Cambridge, MA, 2003.
20. J Vermaak, S Godsill, and A Doucet. Transdimensional sequential Monte Carlo. Technical report, Signal Processing Group, Cambridge University Engineering Department, Trumpington Street, Cambridge, July 2003.