Bernoulli Filter for Extended Target in Clutter Using Poisson Models

CAI Fei; FAN Hongqi; FU Qiang

doi:10.1049/cje.2015.04.017

Volume 24 Issue 2

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CAI Fei, FAN Hongqi, FU Qiang, “Bernoulli Filter for Extended Target in Clutter Using Poisson Models,” Chinese Journal of Electronics, vol. 24, no. 2, pp. 326-331, 2015, doi: 10.1049/cje.2015.04.017

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CAI Fei, FAN Hongqi, FU Qiang, “Bernoulli Filter for Extended Target in Clutter Using Poisson Models,” Chinese Journal of Electronics, vol. 24, no. 2, pp. 326-331, 2015, doi: 10.1049/cje.2015.04.017

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Bernoulli Filter for Extended Target in Clutter Using Poisson Models

doi: 10.1049/cje.2015.04.017

ATR Key Laboratory, National University of Defense Technology, Changsha 410073, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61101186).

Publish Date: 2015-04-10

Abstract

Abstract

This paper presents a joint detection and tracking filter for a single extended target in the presence of clutter measurements and missed detections. The filter is obtained by adapting the Poisson extended target model into the Bernoulli filter proposed by Mahler. The resulted filter is an optimal extended target joint detection and tracking filter. Predictor and corrector are derived follows the random set filtering framework. A particle filter implementation is presented, in which simplification methods are used to make it easy to be realized. Simulation results show that the proposed filter is effective at detection and tracking of extended target in dense clutter backgrounds.
- Extended target,
- Target detection,
- Target tracking,
- Random set

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References(15)

References

K. Gilholm and D. Salmond, “Spatial distribution model for tracking extended objects”, IEE Proceedings-Radar, Sonar and Navigation, Vol.152, No.5, pp.364-371, 2005.

J.W. Koch, “Bayesian approach to extended object and cluster tracking using random matrices”, IEEE Transactions on Aerospace and Electronic Systems, Vol.44, No.3, pp.1042-1059, 2008.

R. Mahler, “Phd filters for nonstandard targets, I: Extended targets”, 12th International Conference on Information Fusion, Seattle, WA, pp.915-921, 2009.

K. Granström and U. Orguner, “A phd filter for tracking multiple extended targets using random matrices”, IEEE Transactions on Signal Processing, Vol.60, No.11, pp.5657-5671, 2012.

C. Lundquist, K. Granström and U. Orguner, “An extended target cphd filter and a gamma gaussian inverse wishart implementation”, IEEE Journal of Selected Topics in Signal Processing, Vol.7, No.3, pp.472-483, 2013.

K. Gilholm, S. Godsill, S. Maskell and D. Salmond, “Poisson models for extended target and group tracking”, Signal and Data Processing of Small Targets, SPIE, Vol.5913, 2005.

M. Baum and U.D. Hanebeck, “Random hypersurface models for extended object tracking”, Proc. IEEE Int. Symp. Signal Process. Inf. Technol. (ISSPIT), Ajman, United Arab Emirates, pp.178-183, 2009.

R.P. Mahler, Statistical Multisource-Multitarget Information Fusion, Artech House Boston, Norwood, MA, 2007.

Y. Fu, J. Long and W. Yang, “Maneuvering multi-target tracking using the multi-model cardinalized probability hypothesis density filter”, Chinese Journal of Electronics, Vol.22, No.3, pp.634-640, 2013.

A. Swain and D. Clark, “The single-group phd filter: An analytic solution”, Proc. 14th Int. Conf. on Information Fusion, Chicago, USA, 2011.

B. Ristic and J. Sherrah, “Bernoulli filter for joint detection and tracking of an extended object in clutter”, IET Radar Sonar Navig., Vol.7, No.1, pp.26-35, 2013.

B. Ristic, B.-T. Vo, B.-N. Vo and A. Farina, “A tutorial on bernoulli filters: Theory, implementation and applications”, IEEE Transactions on Signal Processing, Vol.61, No.13, pp.3406-3430, 2013.

N. Gordon, D. Salmond and A. Smith, “Novel approach to nonlinear/non-gaussian bayesian state estimation”, IEE Proceedings F-Radar and Signal Processing, Vol.140, No.2, pp.107-113, 1993.

D. Salmond and H. Birch, “A particle filter for track-beforedetect”, Proceedings of the American Control Conference, Arlington, VA, 2001.

G. Kitagawa, “Monte carlo filter and smoother for non-gaussian non-linear state space models”, J. Comput. Graph. Statist., Vol.5, No.1, pp.1-25, 1996.

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