Comparison of sampling-based algorithms for multisensor distributed target tracking

Proceedings of the 6th International Conference on Information Fusion, FUSION 2003

Volumn 1, Issue , 2003, Pages 114-121

  Nguyen, Trang M ^a   Jilkov, Vesselin P ^a   Li, X Rong ^a  

^a UNO College of Engineering   (United States)

Author keywords

Multisensor data fusion; Nonlinear filtering; Particle filter; Target tracking; Unscented kalman filter; Unscented particle filter

Indexed keywords

ALGORITHMS; INFORMATION FUSION; MONTE CARLO METHODS; NONLINEAR FILTERING;

DISTRIBUTED TARGET TRACKING; DISTRIBUTED TRACKING; MULTISENSOR DATA FUSION; MULTISENSOR ENVIRONMENT; PARTICLE FILTER; SAMPLING-BASED ALGORITHMS; UNSCENTED KALMAN FILTER; UNSCENTED PARTICLE FILTERS;

TARGET TRACKING;

EID: 84901677485     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIF.2003.177435     Document Type: Conference Paper

References (16)

1. X. R. Li and Y. Bar-Shalom. Design of an Interacting Multiple Model Algorithm for Air Traffic Control Tracking. IEEE Trans. Control Systems Technology, 1(3):186-194, Sept. 1993. Special issue on Air Traffic Control.
2. Y. Bar-Shalom, X. R. Li, and T. Kirubarajan. Estimation with Applications to Tracking and Navigation: Theory, Algorithms, and Software. Wiley, New York, 2001.
3. 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.
4. A. Doucet, N. de Frietas, and N. Gordon, editors. Sequential Monte Carlo Methods in Practice. Statistics for Engineering and Information Science. Springer- Verlag, New York, 2001.
5. Special Issue on Monte Carlo Methods for Statistical Signal Processing. IEEE Trans. Signal Processing, 50(2), Feb. 2002.
6. S. J. Julier and J. K. Uhlmann. A New Approach for Filtering Nonlinear Sytems. In Proceedings of the 1995 American Control Conference, pages 1628- 1632, Seattle, WA, 1995.
7. S. Julier, i. Uhlmann, and H. F. Durrant-Whyte. A New Method for Nonlinear Transformation of Means and Covariances in Filters and Estimators. IEEE Trans. Automatic Control, AC-45(3):477-482, Mar. 2000.
8. E. A. Wan and R. Van der Merwe. The Unscented Kalman Filter. In Kalman Filtering and Neural Networks, chapter 7. Wiley, 2001.
9. R. V. Merwe, A. Doucet, N. De Freitas, and E. Wan. The Unscented Particle Filter. Technical Report CUED/F - INPENG/TR 380, Cambridge University Engineering Department, 2000. Also in: Adv. Neural Inform. Process. Syst., Dec. 2000.
10. X. R. Li and V. P. Jilkov. A Survey of Maneuvering Target Tracking: Dynamic Models. In Proc. 2000 SPIE Conf on Signal and Data Processing of Small Targets, vol. 4048, pages 212-236, Orlando, Florida, USA, April 2000.
11. X. R. Li, Y. M. Zhu, and C. Z. Han. Unified Optimal Linear Estimation Fusion-Part I: Unified Models and Fusion Rules. In Proc. 2000 International Conf. on Information Fusion, pages MoC2.10~MoC2.17, Paris, France, July 2000.
12. A. Doucet, S. Godsil, and C. Andrieu. On Sequential Monte Carlo Sampling Methods for Bayesian Filtering. Statistics and Computing, 10(3): 197-208.
13. M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp. A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking. IEEE Trans. Signal Processing, 50(2): 174-188, Feb. 2002.
14. W. R. Gilks and C. Berzuini. Following a Moving Target-Monte Carlo Inference for Dynamic Bayesian Models. J. Royal Stat. Soc. B, 63:127-146, 2001.
15. X. R. Li and P. Zhang. Optimal Linear Estimation Fusion-Part III: Cross-Correlation of Local Estimation Errors. In Proc. 2001 International Conf. on Information Fusion, pages WeBl.ll-WeB1.18, Montreal, QC, Canada, Aug. 2001.
16. Y. Bar-Shalom and X. R. Li. Estimation and Tracking: Principles, Techniques, and Software. Artech House, Boston, MA, 1993. (Reprinted by YBS Publishing, 1998).