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Tiancheng Li
School of Mechatronics, Northwestern Polytechnical University
China
Shudong Sun
School of Mechatronics, Northwestern Polytechnical University
China
Vol. 2 No. 4 (2013), Articles, pages 31-40
DOI: https://doi.org/10.14201/ADECAIJ2013173140
Copyright

Abstract

Capturing new targets that spontaneously appear in the multi-target tracking (MTT) scene requires a formation of TBI (target birth intensity) item in the PHD (probability hypothesis density) equations. That is, in the particle implementation of the PHD filter, a number of new particles with a certain weight mass are added to the underlying particle set during the propagation of the PHD. In general, TBI is assumed to hold for the same magnitude at all scans. This ad-hoc option is simple but is not always desirable. In this paper, a measurement-driven adaptive mechanism is proposed that determines the magnitude of TBI in real time based on the estimated number of new-born targets, which is calculated by employing the newest measurements. Simulation demonstration of the particle PHD filter has been provided.

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References

Y. Bar-Shalom, X. R. Li, and T. Kirubarajan, Estimation with Applications to Tracking and Navigation: Theory Algorithms and Software, Wiley, John Wiley & Sons, 2004.

R. Mahler, "Multi-target Bayes filtering via first-order multi-target moments," IEEE Trans. Aerosp. Electron. Syst., vol. 39, no. 4, pp. 1152–1178, 2003.

http://dx.doi.org/10.1109/TAES.2003.1261119

R. Mahler, "PHD filters of higher order in target number," IEEE Trans. Aerosp. Electron. Syst., vol. 43, no. 4, pp. 1523–1543, 2007.

http://dx.doi.org/10.1109/TAES.2007.4441756

T. Li, S. Sun and M. Bolić, Algorithm Design for Parallel-Processing Implementation of the SMC-PHD Filter, submitted to IEEE Transactions on Signal Processing, preprint is available at https://sites.google.com/site/tianchengli85/publications/current-work/preprint.

B. Ristic, D. Clark, B.-N. Vo and B.-T. Vo, "Adaptive Target Birth Intensity for PHD and CPHD Filters,"IEEE Trans. Aerosp. Electron. Syst., vol. 48, no. 2, pp. 1656-1668, 2012.

http://dx.doi.org/10.1109/TAES.2012.6178085

K. Punithakumar, T. Kirubarajan, and A. Sinha, "Multiple-Model Probability Hypothesis Density Filter for Tracking Maneuvering Targets," IEEE Trans. Aerosp. Electron. Syst., vol. 44, no. 1, p.87-98, 2012.

http://dx.doi.org/10.1109/TAES.2008.4516991

R. Mahler, B.-T. Vo, and B.-N. Vo, "CPHD Filtering With Unknown Clutter Rate and Detection Profile," IEEE Transactions on Signal Processing, Vol. 59, No. 8, Aug. 2011, pp. 3497-3513.

http://dx.doi.org/10.1109/TSP.2011.2128316

X. Chen, R. Tharmarasa, M. Pelletier, and T. Kirubarajan, "Integrated Clutter Estimation and Target Tracking using Poisson Point Processes," IEEE Trans. Aerosp. Electron. Syst., vol. 48, no. 2, p.1210-1235, 2012.

http://dx.doi.org/10.1109/TAES.2012.6178058

B.-N. Vo, S. Singh, and A. Doucet, "Sequential Monte Carlo methods for multi-target filtering with random finite sets," IEEE Trans. Aerosp. Electron. Syst., vol. 41, no.4, pp. 1224–1245, 2005.

http://dx.doi.org/10.1109/TAES.2005.1561884

M. Tobias and A. D. Lanterman "Techniques for birth-particle placement in the probability hypothesis density particle filter applied to passive radar," IEE Proc. Radar Sonar Navig., vol. 2, no. 5, pp. 351–365, 2008.

http://dx.doi.org/10.1049/iet-rsn:20070051

E. Maggio, M, Taj and A. Cavallaro, "Efficient multi-target visual tracking using random finite sets," IEEE Trans. Circuits and Systems for Video Technology., vol. 18, no. 8, pp. 1016– 1027, 2008.

http://dx.doi.org/10.1109/TCSVT.2008.928221

Y. Wang, Z. Jing, S. Hu, and J. Wu, Detection-guided multi-target Bayesian filter," Signal Processing, vol. 92, no. 3, pp. 564– 574, 2012.

http://dx.doi.org/10.1016/j.sigpro.2011.09.002

X. Zhou, Y.F. Li, and B. He, "Entropy distribution and coverage rate-based birth intensity estimation in GM-PHD filter for multi-target visual tracking," Signal Processing, vol.94, pp. 650 -660, 2014.

http://dx.doi.org/10.1016/j.sigpro.2013.08.002

J. H. Yoon, D. Y. Kim, S. H. Bae, and V. Shin, "Joint Initialization and Tracking of Multiple Moving Objects Using Doppler Information," IEEE Transactions on Signal Processing, Vol. 59, no. 7, 3447-3452, July 2011.

http://dx.doi.org/10.1109/TSP.2011.2132720

S. Oh, S. Russell, and S. sastry, "Markov Chain Monte Carlo data association for Multi-Target Tracking," IEEE Trans. Automatic Control, vol. 54, no. 3, pp. 481-496, 2009.

R. Mahler, ""Statistics 102" for multisource-multitarget detection and tracking," IEEE Journal of Selected Topics in Sig. Processing, vol. 7, no. 3, pp. 376–389, 2013.

J. Houssineau, and D. Laneuville, "PHD filter with diffuse spatial prior on the birth process with applications to GM-PHD filter," Proc. 13th Int. Conf. Information Fusion, Edinburgh, 2010.

B.-N. Vo, and W.-K. Ma, "The Gaussian mixture probability hypothesis density filter," IEEE Trans. Signal Process., vol. 54, no. 11, pp. 4091–4104, 2006.

http://dx.doi.org/10.1109/TSP.2006.881190

T. Li, M. Bolic, P. Djuric, Resampling methods for particle filtering, IEEE Signal Processing Magazine, to appear.

T. Li, S. Sun, T.P. Sattar and J. M. Corchado. Fight sample degeneracy and impoverishment in particle filters: A review of intelligent approaches, Expert Systems With Applications, 2014, to appear, DOI:10.1016/j.eswa.2013.12.031.

http://dx.doi.org/10.1016/j.eswa.2013.12.031

M. Beard, B.-T. Vo, B.-N. Vo, and S. Arulampalam, "Gaussian mixture PHD and CPHD filtering with partially uniform target birth," Proc. 15th Int. Conf. Information Fusion, Singapore, July 2012.

X. Zhou, Y.F. Li, B. He, and T. Bai, "GM-PHD-Based Multi-Target Visual Tracking Using Entropy Distribution and Game Theory," IEEE Trans. on Industrial Informatics, vol. 99, no. PP, pp. 1-12, 2013.

T. Li, S. Sun and T. Sattar, "High-speed sigma-gating SMC-PHD filter," Signal Processing, vol.93, no.9, pp. 2586-2593, 2013.

http://dx.doi.org/10.1016/j.sigpro.2013.03.011

M. Tobias and A. D. Lanterman, "Probability hypothesis density-based multitarget tracking with bistatic range and Doppler observations," IEE Proc., Radar Sonar Navig., vol. 152, no. 3, pp. 195–205, 2005.

http://dx.doi.org/10.1049/ip-rsn:20045031

N. Whiteley, S. Singh, and S. Godsill, "Auxiliary particle implementation of probability hypothesis density filter," IEEE Trans. Aerosp. Electron. Syst., vol. 46, no. 3, pp. 1437–1454, 2010.

http://dx.doi.org/10.1109/TAES.2010.5545199

T. Li, S. Sun, M. F. Siyau and J. M. Corchado, Multi-EAP: asymptotically optimal estimate extraction method for the SMC-PHD filter, submitted to Digital Signal Processing, 2013. Preprint at https://sites.google.com/site/tianchengli85/publications/current-work/preprint.

D. Schuhmacher, B.-T. Vo, and B.-N. Vo, "A consistent metric for performance evaluation in multi-object filtering," IEEE Trans. Signal Process., vol. 56, no. 8, pp. 3447– 3457, 2008.

http://dx.doi.org/10.1109/TSP.2008.920469