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Języki publikacji
Abstrakty
Track coalescence is a phenomenon that occurs in multi-target tracking applications where certain types of manoeuvres performed simultaneously by several targets can utterly confuse algorithms that track their positions. In its simplest form, the phenomenon occurs when two similar objects, initially well separated, get close to each other and follow similar manoeuvres for a period of time sufficient to confound the tracking algorithm so that when the objects finally depart from each other the tracking algorithm is prone to provide erroneous estimates. This two-target track coalescence is discussed in this paper with the focus on the compound coalescence, when two identical tracks follow the midpoint of two well separated targets. First, the problem is illustrated on a classic problem of tracking two targets manoeuvring in a clutter, which is modeled as a nonlinear stochastic system. It is shown how, otherwise accurate and precise, estimates obtained by a standard particle filter eventually collapse leading to the coalescence of tracks. The phenomenon is given theoretical explanation by the analysis of Bayesian update operator acting on L2-space of probability densities that reveals that the coalescence is an unavoidable consequence of the probabilistic mixing between distributions describing positions of two targets. Finally, the practical consequences of these theoretical results are discussed together with potential approaches to deal with track coalescence in real applications.
Słowa kluczowe
Rocznik
Tom
Strony
32--45
Opis fizyczny
Bibliogr. 17 poz., wykr.
Twórcy
autor
- Department of Complex Systems, National Centre for Nuclear Research, ul. A. Sołtana 7, 05–400 Otwock-Świerk, Poland
Bibliografia
- 1. Kalman, R. E.: A new approach to linear filtering and prediction problems, Journal of basic Engineering, vol. 82, pp. 35-45, 1960.
- 2. Ljung, L.: Asymptotic behavior of the extended Kalman filter as a parameter estimator for linear systems. IEEE Transactions on Automatic Control, vol. 24, pp. 36-50, 1979.
- 3. Wan E. A., Van Der Merwe R.: The unscented Kalman filter for nonlinear estimation. In Adaptive Systems for Signal Processing, Communications, and Control Symposium (AS-SPCC), pp. 153-158, 2000.
- 4. Alspach D. L., Sorenson H., Recursive Bayesian Estimation Using Gaussian Sums, Automatica, vol. 7, pp. 465-479, 1971.
- 5. Stano, P. M., Lendek, Zs., Braaksma, J., Babuska, R., de Keizer, C., den Dekker, A, J. Parametric Bayesian filters for nonlinear stochastic dynamical systems: A survey. IEEE transactions on cybernetics, vol. 43, pp. 1607-1624, 2013.
- 6. Arulampalam S., Maskell S., Gordon N., Clapp T.: A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking, IEEE Transactions on Signal Processing, issue 50, pp. 174-188, 2002.
- 7. Blom H. A., Bloem E. A.: Optimal Decomposed Particle Filtering of Two Closely Spaced Gaussian Targets, Proceedings of the IEEE Conference on Decision and Control and European Control Conference (CDC-ECC), pp. 7895-7901, 2011.
- 8. Blom H. A., Bloem E. A., Musicki, D.: JIPDA: Automatic Target Tracking Avoiding Track Coalescence, IEEE Transactions on Aerospace and Electronic Systems, vol. 51, pp. 962-974, 2015.
- 9. Blom H. A., Bloem E. A.: Decomposed Particle Filtering and Track Swap Estimation in Tracking Two Closely Spaced Targets, Proceedings of the 14th International Conference on Information Fusion (FUSION), pp. 1-8, 2011.
- 10. Ristic B., Arulampalam S., Gordon N.: Beyond the Kalman Filter: Particle Filters for Tracking Application. Artech House, 2004.
- 11. Doucet A., Godsill S., Andrieu, Ch.: On Sequential Monte Carlo Sampling Methods for Bayesian Filtering, Statistics and Computing, vol. 10, pp. 197-208, 2000.
- 12. Stano P. M., Tilton A. K., Babuska R.: Estimation of the Soil-dependent Time-varying Parameters of the Hopper Sedimentation Model: The FPF versus the BPF, Control Engineering Practice, vol. 24, pp. 67-78, 2014.
- 13. Stano P. M., Lendek Zs., Babuska R.: Saturated Particle Filter: Almost Sure Convergence and Improved Resampling, Automatica, vol. 49, pp. 147-159, 2013.
- 14. Stano P. M., den Dekker, A. J., Lendek Zs., Babuska R.: Convex Saturated Particle Filter, Automatica, vol. 50, pp. 2494-2503, 2014.
- 15. Yang T., Blom H. A., Mehta P. G.: Interacting Multiple Model-Feedback Particle Filter for Stochastic Hybrid Systems, Proceedings of the IEEE Annual Conference on Decision and Control (CDC), pp. 7065-7070, 2013.
- 16. Svensson L., Svensson D., Guerriero M., Willett P.: Set JPDA Filter for Multitarget Tracking, IEEE Trans. Signal Process, vol. 59, pp. 4677-4691, 2011.
- 17. Chen X., Li Y., Li Y., Yu J., Li, X.: A Novel Probabilistic Data Association for Target Tracking in a Cluttered Environment, Sensors, vol. 16, pp. 2180, 2016.
Typ dokumentu
Bibliografia
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